Data Quality Audit (DQA) Guidelines (Health Management Information System)

Transcription

1 Data Quality Audit (DQA) Guidelines (Health Management Information System) Prepared by Ministry of Health Directorate of Policy and Planning Monitoring & Evaluation Unit Ndeke House P.O. Box Lusaka Version 1,

2 Disclaimer: This publication was made possible through support provided by USAID/Zambia, U.S. Agency for International Development, under the terms of the Cooperative Contract No. GHH (Task Order No. GHS ), Zambia Integrated Systems Strengthening Program. The opinions expressed herein are those of the authors and do no necessary reflect the views of those of the U.S. Agency for International Development. i

3 Foreword The Government of the Republic of Zambia with support from partners has continued to develop a strong health information management system (HMIS). Development of a strengthened HMIS is deemed pivotal in supporting evidence-based planning, resource allocation and policy implementation at all levels of health care delivery. An optimal HMIS system ensures that the data collected, reported and utilized by the health care delivery system are of unquestionable quality so as to effectively direct decision-making at patient/client, health care delivery system and health system management levels. To strengthen Zambia s HMIS, the government has undertaken two major revisions and a number of minor reviews of the HMIS over the past 15 years in the quest to respond to the needs of the health care delivery system. Despite these revisions, data in reports from several health facilities was alleged to be of low quality, which resulted in limited output of accurately generated key indicators. This perception of the quality of data remained unconfirmed over years due to lack of official standardized data assessment documentation. (Over the past years, data quality assessments have been undertaken but used variations in approaches, resulting into inconsistencies). As a result, it is difficult to tell the extent of the problem of data quality and whether this effort has contributed to improving data quality or not. Against this background, the Ministry of Health (MOH) has developed this first edition of standardized Data Quality Audit Guidelines with the aim to provide clear guidance to programme managers on how to conduct data quality audits within and to their health institutions. These guidelines are therefore aimed at standardizing the process of conducting data quality assessments and further strengthening the systems for managing information in all our health institutions. This initiative will also provide a platform for documenting strengths and shortcomings in the systems (via monitoring) for consideration during major revisions of the system in the future. I therefore urge all programme managers at all levels to use these guidelines each time they undertake data quality audits of their health institutions. This practice will not only improve data management and usage at all levels, but will also ensure that data being collected and presented is of good quality and can inform policy decisions. Dr.Davy M Chikamata Permanent Secretary Ministry of Health ii

4 Acknowledgement A number of people contributed to the successful production of this first edition of thedata Quality Audit Guidelinesfor use by the Ministry of Health (MOH) and its cooperating and/or implementing partners. The invariable dedication and input of personnel from the national, provincial and district level and cooperating partners was key in ensuring production of a timely and quality document. The MOH would therefore like to thank the following individuals who were key in the development of the first edition of the guidelines: Mr. Calvin Kalombo (Senior M&E Officer), Mrs. Katongo Mumbi Silwizya (Health Information Analyst), Ms. Emily Moonze (Management Specialist Team Leader- ZISSP), Masauso Phiri (Data Management Officer- MOH-HQ), Peter Funsani (M&E Officer- MOH-HQ), Mr. Charles Kachaka (District Health Information Officer - Kasama), Mr.DarfyWillie Chaponda (District Health Information Officer - Chikankata), Ms.ChumaryMunyinya (District Health Information Officer - Ndola), Mr.MutaleMwango (Senior Health Information Officer - Western), Mr. Kingsley Kapemfu (Senior Health Information Officer - Copperbelt),KawanaLisulo (Senior Health Information Officer - Chainama Hospital), JosephatKunda (Senior Health Information Officer- Southern), Janet Phiri (District Health Information Officer Sesheke), and Paul Munsanje (Health Information Officer - Lewanika General Hospital). Special thanks go to the United States Agency for International Development (USAID) through the Zambia Integrated Systems Strengthening Program (ZISSP) for providing financial and technical Support for the development and printing of these guidelines. The editorial team comprised of: Dr. Christopher Simoonga (Director of Policy and Planning- MOH/HQ), Mr. Chipalo Kaliki (Deputy Director, Monitoring and Evaluation-MOH/HQ),Mr. Calvin Kalombo (Senior M&E Officer), Emily Moonze (Management Specialist Team Leader ZISSP) and Elizabeth C. Jere (Technical Writer ZISSP). Finally, I would like to acknowledge the work of the consultant, Paul Chishimba, through ZISSP support, for ensuring the successful completion of these first-ever Data Quality Audit Guidelines. Dr. Christopher Simoonga Director Policy and Planning Ministry of Health iii

5 Abbreviations AIDS CHN CI CL DE DHIS DHO DMO DQA GAVI GFATM HIA HIS HIV HMIS M&E MCDMCH MDG MOH ND NHSP PA PHO PMO PMTCT PRISM RHIS SOP TB TSS UNAIDS UNICEF USAID WHO ZISSP Acquired Immune Deficiency Syndrome Child Health and Nutrition Confidence Interval Confidence Level Data Element District Health Information System District Health Office District Medical Office(r) Data Quality Audit Global Alliance on Vaccines and Immunization Global Fund to Fight AIDS, TB, and Malaria Health Information Aggregation Health Information System Human Immuno-deficiency Virus Health Management Information System Monitoring and Evaluation Ministry of Community Development, Mother and Child Health Millennium Development Goals Ministry of Health Notifiable Disease National Health Strategic Plan Performance Assessment Provincial Health Office Provincial Medical Office(r) Prevention of Mother To Child Transmission Performance of Routine Information System Management Routine Health Information System Standard Operating Procedure Tuberculosis Technical Supportive Supervision Joint United Nations Programme on HIV/AIDS United Nations Children s Fund United States Agency for International Development World Health Organisation Zambia Integrated Systems Strengthening Program iv

6 Table of Contents Foreword... ii Acknowledgement... iii Abbreviations... iv Table of Contents... v List of Tables... vii List of Figures... vii Introduction... 1 Background... 1 General Overview... 2 Objectives Of Conducting Data Auditing... 2 Conceptual Framework... 2 Methodology Data Quality Defined What Data to Collect... 5 Audit Outputs 9 Ethical Considerations... 9 Implementation Overview... 9 Stage 1: Preparing for the Audit Step 1 Selecting Data Elements Sampling Data Elements on the Service Delivery Aggregation Form (HIA2) Open a blank Excel Spread sheet Use the sampling frame in Annex 4 to identify which data elements represent the sampled numbers in Table 5 from the CHN list Repeat 1 and 2 above for each programme to select the desired sample size under each area as stipulated in column (d) of Table Sampling Data Elements on the Disease Aggregation Form (HIA1) Step 2 Selecting Data Audit Units and Forming Teams Considerations in choosing sites What to do if a site does not provide some services Who Should Be on The Auditing Team Step 3 Review Relevant Documentation Previous audit reports Feedback reports/dispatches Most recent PA/TSS report Annual/Revised Action Plan Additional Information from key informants Step 4 Prepare Data for the Audit Auditing the Facility Level Auditing the District Level Step 5 Prepare for Site Visits Information for the District Medical Office Information for the HEALTH Facility Stage 2: Fieldwork - District Health Office v

7 Step 6 Assess Data Management Structures and Systems Step 7 Trace and Verify Data with Facility Reports Stage 3: Fieldwork - Health Facility Step 8 Assess Data Collection, Storage and Reporting Systems Step 9 Trace and Verify Data with Primary Data Sources Stage 4: Finalise the Audit Step 10 Preliminary Data Analysis and Presentation (Onsite) Health Facility District Health Office Step 11 Preparation of the Audit Report Step 12 Discuss Report and Formulate Recommendations Step 13 Develop a Data Quality Improvement Plan References 24 Annex 1: Facility Assessment Tool Annex 2: District Assessment Tool Annex 3: Service Deliver Sampling Frame HIA Annex 4: Diseases Sampling Frame HIA Annex 5: Graphing Templates - Samples Annex 6: Performance Assessment Guides vi

8 List of Tables Table 1: Data Needs - Dimensions of Data Quality and HIS Processes... 4 Table 2: Description of the data quality assessment tool - health facility... 6 Table 3: Description of the data quality assessment tool District... 8 Table 4: Sample Distribution Table for Data Elements on HIA Table 5: Example of Sampled Numbers - Child Health and Nutrition Table 6: Example - Matching Sampled Numbers with Data Elements Table 7: Sample Distribution Table for Data Elements on HIA Table 8: Facility Audit - Things to Prepare Table 9: District Audit - Things to Prepare Table 10: Example on how to complete the questionnaires and score each section Table 11: Facility Performance Assessment Guide Table 12: District Performance Assessment Guide Table 13: Problem Analysis Matrix List of Figures Figure 1: Performance of Routine Information System Management Framework (Annotated)... 3 Figure 2: The Zambia Information Cycle vii

9 Introduction Background The Government of the Republic of Zambia through its line ministry, the Ministry of Health (MOH) has as a vision of : providing Zambians with equity and cost effective, quality health care as close to the family as possible, by enhancing service delivery efficiency through decentralisation. Due to resource constraints, the need to use resources efficiently and the demand for good governance and transparency, the need for evidence-based decision-making cannot be over-emphasised. As MOH is in the process of implementingitsnational Health Strategic Plan (NHSP)( )and its counterpart ministry, the Ministry of Community Development, Mother and Child Health (MCDMCH),having recently (July 2013) launched its Strategic Plan for , and with the international community being in its last 18 months of the Millennium Development Goals (MDG) era, thecurrent focus is on conceptualising the post-mdg period that places emphasis on Universal Health Coverage. All these factors place a responsibility on the country for the development and management of an information system capable of meeting the data needs of these aspirations. This like many other developing countries, Zambia has been exposed to the practice of performancebased release of programme funding that is requested by international funding agencies, such as the Global Alliance on Vaccines and Immunization (GAVI) and the Global Fund to Fight AIDS, TB, and Malaria (GFATM). This condition, coupled with emerging health challenges such as Noncommunicable Diseases, requires increasingly higher amounts of quality information. In reforming the health sector, the government (with support from its partners) has placed information for decision-making at the centre of all forms of planning at each level of health care delivery. In the past 15 years, the government has undertaken two major revisions and a number of minor reviews to the health management information system (HMIS) in the quest to respond to the needs of the health care delivery system. Despite these revisions, data in the reports from several districts was perceived to be of low quality, which resulted in limited output of accurately-generated key indicators. This perception on the quality of data remained unconfirmed over years due to lack of official documentation (although possible reasons leading to compromised data quality has been known for many years). Literature has also cited quality of data as one of the major contributors to post-implementation failures of many systems (Mutemwa, 2006; Odhiambo-Otieno, 2005) due to its circular relationship with data utilisation data are not used due to poor quality, which leads to deteriorating data quality and subsequently reduced confidence in the data. Efforts towards ensuring HMIS data quality are as old the 1990s health reforms. The MOH has come up with various initiatives that are intended to ensure that the data collected, reported and utilised by the health care delivery system are of unquestionable quality so as to effectively direct decision-making at patient/client, health care delivery system and health system management levels. These initiatives, among others, include the following: i. Revising the minimum dataset (to lessen the data handling burden); ii. Revising data collection and collation forms (to simplify recording); iii. iv. Producing protocols and guidelines for data collection, collation and reporting (to standardize data management); Integrating data quality and consistent checks in the electronic databases (to trap errors at data entry) and; v. Instituting routine quarterly/semi-annual quality checks on the data that have already been collected and reported (data quality audits or assessments). 1

10 Although data quality audits have been going on (with varying intentions) for over five years, it is difficult to tell the extent of the problem of data quality and whether this effort has contributed to improving data quality. This gap is partly due to the absence of standards and protocols for conducting these assessments and guidance on how to use accompanying results in improving data quality. It is against this background that the MOH has decided to develop a standardised approach to assessing the quality of the HMIS data to provide a basis for identifying underlying weaknesses for possible interventions. This initiative will also provide a platform for documenting strengths and shortcomings in the system (via monitoring) for consideration during major revisions of the system in the future. General Overview Every data collection system is prone to errors resulting from design oversights or people-based mistakes during data collection, processing and transmission. Therefore, before data are transformed into a state useful for decision making, all inherent errors should be removed (data processing). Data errors associated with the collection stage (in the information cycle) are the most difficult to remedy. It is important therefore that as much as possible, errors are reduced to the minimum levels possible, starting with individual patient data. This is only achievable through adequate training and consistent use of standardised guidelines at each stage. Objectives of Conducting Data Auditing Data audit is one of the stopgap measures used to enhance data integrity once data have been collected and before they can be put to use. The objectives of conducing data quality auditing are as follows: 1. To quickly CONFIRM: a. the authenticity of data that have been reported through the district by service delivery points b. whether the system adequately addresses basic determinants of data quality at each stage of routine health information processes. 2. To USE THE RESULTS from the audit as input in identifying activities for improving quality of data (and indirectly quality of services) through targeted decision-making informed by data. 3. To provide a standardised quantity that can be compared between or across time points and places as way of MONITORING the performance of the HMIS in producing quality data. Conceptual Framework The PRISM (Performance of Routine Information System Management) Framework is one of the most recent innovations to guide the designing, strengthening and evaluating routine health information systems (RHIS) (Aqil, A., 2009). It identifies good quality data and continuous information useas the two desired outputs of a health information system (HIS) pipeline. The framework emphasises that the performance of any HIS is affected by three categories of determinants namely: organisational, technical and behavioural. These determinants have been identified as those responsible for influencing the successful execution of the HIS processes (collection, transmission, processing, analysis, display, quality checking and feedback). The hierarchical presentation of processes in the framework is drawn from the common practice in most HIS, whereby data are collected routinely in the course of providing a service (through providerpatient/client interactions), and once satisfied with quality, these data are then summarised monthly/quarterly and transmitted to the district for computer data entry, where processing, analysis and presentation is done. During processing, analysis and presentation, data are checked for quality. It is then on this basis that feedback is provided to the source facilities. Good quality data is one of the two outputs of HIS processes. For the HIS processes to produce quality data, the PRISM framework suggests that a proper mix of inputs (RHIS determinants) should 2

11 be attained for optimal execution of HIS processes. Good quality data therefore is influenced directly by HIS processes and indirectly by HIS determinants (Figure 1). Figure 1: Performance of Routine Informationn System Management Framework (Annotated) The Data Quality Audit (DQA) procedures and tools presented in this document are therefore meant to: 1. Validate the quality of the data, 2. Assess the processes and associated determinants thatt produces that data, and 3. Develop action plans to improve both. Methodology 1. Dataa Quality Defined Data quality is definedd through four dimensions: relevance, completeness, timeliness and accuracy (Lippeveld et al., 2000). Data auditing is defined as a process of verifying the quality of reported aggregate HMIS data. This typicallyy requires field visits to institutions thatt reported the data in orderr to check these data against client or other individual records (UNAIDS 2008). Completeness is measured at two levels: At facility level, completeness is measured by reviewing the forms to check whether all the data elements that should have been reported are reported during a reporting period. At subnational and national levels, completeness is assessed in terms of completeness of reports on dataa elements and reporting units. Timeliness is assessed in terms of submission of the reports by an accepted deadline at each level. Accuracy is measured by comparing data that has been reported or compiled against the primary source documents at the point of collection and triangulation of the reported data with other data sources, if available. Relevance is assessed through the usefulnesss of data collected against the information requirements of management functions. Since this manual focuses on routine assessment of

12 quality of the HIS data, this attribute is not discussed in detail as it is a system design issue. (This dimension can be reviewed in a major evaluation of the HMIS). Some frameworks extend the space to include the following dimensions: reliability, precision, integrity and confidentiality (Measure Evaluation, 2008). Although these are defined here, they are not discussed as standard attributes of data quality because these properties border on the design of the system rather than practice. However, when conducting a full system evaluation the following dimensions may be considered: Reliability is assessed by checking whether the data generated by an information system are based on protocols and procedures that do not change according to who is using them and when or how often they are used. The data are reliable because they are measured and collected consistently. Precision means that the data have sufficient detail. For example, if an indicator requires the number of individuals who received HIV counselling& testing and received their test results, by sex of the individual, then the information system would lack precision if it is not designed to record the sex of the individual who received counselling and testing Integrity of data is when the system used to generate them is protected from deliberate bias or manipulation for political or personal reasons. Confidentiality means that clients are assured that their data will be maintained according to standards for data. Personal data should not be inappropriately disclosed or left unsecured. Recognising the indisputable role that HIS processes play in realising the desired quality of data, Table 1 identifies tracers of data quality at each stage of data handling and shows which dimension of data quality is likely to be affected at each administrative level. Table 1: Data Needs - Dimensions of Data Quality and HIS Processes Dimension of Level Data Quality HIS Process Data Collection Data Processing Data Transmission Data Quality Tracer 1. Designated staff to collect data from clients/patients exist 2. Staff collecting data from patients trained in HMIS 3. Standard Operating Procedure (SOP) for data collection exist 4. Indications of use of the SOPs 5. Correct versions of cards/forms and registers used 6. Appropriate timing of updating tally/activity sheets and registers 1. Designated staff to collate/aggregate data exist 2. Staff responsible for data collation/aggregation trained in HMIS 3. SOPs exist for collation/aggregation 4. Correct versions of collation and aggregation forms used 5. Aggregation form bears details on who prepared/ approved and dates 6. A log sheet exists to indicate when each form was entered by the district 1. Facility In-charge/Director is aware of the data flow policy (dates). 2. A log sheet exists to indicate when each form was sent to the district 3. A log sheet exists to show when the dataset was sent to the next level Accuracy Completeness Timeliness Facility District Sub/National 4

13 Dimension of Data Quality Level HIS Process Data Quality Checking Feedback Data Quality Tracer 4. A log sheet exists to indicate when each form/dataset was received 5. A record of staff members who receive the reports at district exists 1. Only one version of the same report(s) for the same period exist 2. Data on Health Information Aggregation (HIA) form is traceable to a tally/activity/collation sheet or register 3. Hard copy report at the district is the same as the one at facility 4. Data on hard copies the same as entries on the computer 5. Hand-summed facility totals are the same as the totals on the database 6. Record exists to show that HMIS problems are reported to the next higher level 1. Proof of feedback from the higher level exists 2. Dates of communication on feedback documented 3. If feedback requested a change in the data, a record of that change exists Accuracy Completeness Timeliness Facility District Sub/National 2. What Data to Collect Using Table 1 as a basis, two questionnaires (Annexes 1 &2) have been formulated one for the facility and the other for the district. Since the intention of this manual is to provide guidance on how each administrative level can be assessed for data quality (as opposed to assessing all the levels at once), procedures have been presented according to the two key levels: health facility and district levels. Although the national data flow procedures include the provincial level in data handling, no alterations to the data are made at this level. It is against this background that the two data collection instruments presented in this documentaremeant for use at facility and district levels. This is intended to strengthen routine data quality monitoring as opposed to the traditional reliance on external evaluations. A. Health Facility Data Quality Assessment Tool This instrument is intended to provide a comprehensive data quality picture about a given facility covering both the determinants and dimensions of data quality. This tool is intended for use at any level of health facilities as long as the selection of data elements for audit takes cognisance of varying bouquet of services provided by each health facility. If the data auditing team has prior 1 information on the sources of data errors, the audit can focus on only those weak aspects. This assessment tool covers two sections: Determinants of Data Quality and Dimensions of Data Quality. A summary description of the tool can be found intable 2 (below). The tool itself can be found inannex 1: Routine Data Quality Audit - Health Facility Assessment Form. 1 Prior knowledge may be based on information from the previous performance assessments and technical support supervisions 5

14 B. District Data Quality Assessment Tool Like the facility form, this instrument is intended to assess the core functions of the district medical office in data management. It covers selected determinants of quality (human and material resources); reporting timeliness (knowledge and practice); reporting completeness (availability of data and data entry coverage); and data accuracy (data entry and data consistency). For the summary description, see Error! Reference source not found. below. The tool itself can be found in Annex 2: Routine Data Quality Audit - District Assessment Form. 6

15 Table 2: Description of the data quality assessment tool - health facility 2 Focus Area Summary Description Section 1: Determinants of Data Quality Availability of trained staff to This section should be administered through collect data from direct observation of the practice or by patients/clients during delivery asking the facility/ward in-charge. A of services B Availability of reference and data collection materials This subsection is meant to assess availability of essential (correct) materials at health facility level. Questions for this section always should be administered. HMIS Feedback A working feedback system provides a live loop an avenue to exchanging experiences and resolving problems between each C administrative layer. Questions in this section look at practice and how this may impact on data quality through data revision and record keeping. Section 2: Dimensions of Data Quality 2.1 Report Timeliness A Knowledge of the HMIS Data Handling Deadlines Practice in meeting reporting B deadlines 2.2 Report Completeness Availability of completed cards, registers, tally/activity sheets and HIA forms C D Data Completeness: Assess whether all required data elements have been entered on the form 2.3 Data Accuracy E F Collation Accuracy: Verifying whether aggregated data on HIA1/2 match the primary data sources Internal data consistency: Whether expected relationships in the dataset are not violated These questions should be asked to the facility in-charges. The subsection measures knowledge and practice towards meeting submission deadlines. Questions under this section are intended to assess whether health facility has a record to show that a service was provided and documented, before reporting to the districts. They are proxy measures for record keeping and filling. This section provides a composite summary of missing data elements, in part or the entire form. This information is collected by comparing aggregated data on the reports sent to the district with the source documents. This is done backwards thus: HIA1 Tally sheets Registers HIA2 Activity sheet/registers This section validates 2.3E in that even when aggregated data and sources match, at times the data are still not correct because the errors were introduced at data collection. Application frequency - Routinely twice per year - When there is a need to follow up on training or planning for refresher training. Due to high staff turnover rates, questions for this section should be asked at every opportunity during PA. Every Assessment Once per year or anytime during PA when a new in-charge is encountered. Every Assessment Every assessment visit Every assessment visit Every assessment visit Every assessment visit 2 The tool can be found in Annex 1: Routine Data Quality Audit - Health Facility Assessment Form. 7

16 Table 3: Description of the data quality assessment tool District 3 Focus Area Summary Description Application frequency Section 1: Determinants of Data Quality A B A-Availability of trained staff to perform management functions B-Availability of Resources: Equipment, Materials and Space Section 2: Dimensions of Data Quality 2.1 Report Timeliness Knowledge of the HMIS Data A Handling Deadlines Practice in meeting reporting B deadlines 2.2 Report Completeness Availability of completed HIA forms C D Data Entry Completeness: Assess whether all reported data have been entered on the computer 2.3 Data Accuracy Data Entry Accuracy: Verifying whether aggregated E data on HIA1/2 match the data on computer Internal data consistency: Whether expected relationships F in the dataset are not violated The set of questions under this section provides a profile on the value the district office has attached to HMIS processes which if not properly handled affect quality of data. This subsection is meant to assess availability of working space, technology and materials. These questions should be asked to the district in-charges. The subsection measures knowledge and practice towards meeting submission deadlines. Questions under this section are intended to assess whether the data reported were actually submitted to the district by their health facilities, and that a record exists. They are proxy measures for record keeping, filling and data entry completeness. This section assesses whether data that was actually submitted by the facilities have been entered into the computer. To shorten the time spent at the district, the audit team should preview the data before visiting the district. SEE IMPLEMENTATION STEPS. This information is collected by comparing aggregated data on the reports received by the district with the data on the DHIS. These section validates 2,2C, 2.2D, 2.3E in that even when aggregated data and sources match, at times the data are still not correct because the errors were introduced at data collection or entry - Routinely twice per year - When there is a need to follow up on training or planning for refresher training. Routinely, during every PA visit Once per year or anytime during PA when a new DMO is encountered Every Assessment Every assessment visit Every assessment visit Every assessment visit Every assessment visit 3 Feedback Although the provincial and national levels cannot correct the data, because of higher skill concentration the two levels should provide oversight of the data from the district by ensuring that those errors that manage to slip through the districts can be quickly brought to their attention. This is important for data quality improvement and enhanced usage. 3 The tool can be found in Annex 2: Routine Data Quality Audit - District Assessment Form. 8

17 Audit Outputs The DQA process is meant to gather and document the status of selected determinants of data quality and the results on the dimensions of data quality by: Completing the facility data quality assessment form (at facility level) and the district assessment form at the district health office. Documenting observations, additional submissions (voluntary or probed) from staff responsible for ensuring quality of data at facility or district levels. Making onsite presentations of summary findings as a way of providing immediate feedback. Preparing an Audit Report that includes and documents findings as gathered by the audit team, conclusions arising from those findings and recommendations following the interpretations. Narratives in the report will be augmented by the summary statistics collected through the assessment forms, covering the following core areas: 1. Determinants of data quality focusing on availability of trained HMIS staff; availability of materials, space and equipment; and adequacy of the feedback mechanism. 2. Availability, Completeness and Timeliness of Reports through the computation of percentage scores on sections and direct comparisons of counts were necessary 3. Accuracy of reported data through calculated ratios (reported to recounted numbers). Dissemination of findings and recommendations to the national, provincial, district and facilities for quality improvement follow-ups. Ethical Considerations The DQA process should strictly adhere to existing Zambian protocols on privacy and confidentiality of data from individual patient/client records. This DQA protocol will not be the first to expose health workers and officers from the health ministry to patient files. As such the requirement to audit the data should not supersede the demand for respecting existing procedures and protocol on data privacy and confidentiality. Implementation Overview The approach presented in this document is premised on the fact that sometimes HIS have design oversights and that, even when they are properly designed, things can go wrong during data handling processes thereby affecting the quality of the intended outputs (data quality and continuous use of data for decision-making). As shown in Figure 2, in the Zambia HMIS, data collection starts with recording of interactions between the provider and the client/patient. Data are recorded onto individual patient/client cards or entered into registers. Then a predefined set of key data elements are either recorded directly or transferred onto activity sheets or tally sheets and later summarised onto aggregation forms. Once the aggregation form has been completed by the facility it is sent to the district medical office for data entry into the computer. Beyond this point, there are no permissible data changes that can be done unless in consultation with the district or the source facility. It is for this reason that this DQA package focuses on routine assessment of data at facility and district levels only. Note: This approach is based on the understanding that the health ministry or with support from partners, commission major evaluations of the data systems. Examples of such major evaluations include those that were done under the Health Metrics Network or before a major revision of the HMIS. 9

18 Figure 2: The Zambia Information Cycle This manual recommends that two approaches be considered when planning for the audit: sequential or standalone. Each of these approaches is described below: 1. Duo-level (Sequential): This approach applies to instances whereby an administrative level higher than the district (e.g. provincial or national) commissions an audit throughout the country or in a given province or a district. In such a case, the audit should start with the district health office and then onto health facilities of interest. The two data collection forms will be administered appropriately. 2. Single-level (Stand-alone): This approach is when the district health office decides to undertake an audit of selected facilities or data elements as a local level initiative, supported by themselves or through a partner. This can either be a planned activity or an adhoc one. Adhoc audits include those activities undertaken because a problem has been identified and an audit is ordered. Following the two approaches described above, the two data collection tools can be administered in the following way: 1. Routine Data Quality Audit - Health Facility Assessment Form: This tool can be used for a single-level (facility) or a duo-level (sequential) audit. In a duo-level audit, results from this tool are used in conjunction with those from the District Data Audit Tool. 2. Routine Data Quality Audit - District Assessment Form: This tool should be used in conjunction with the facility tool by first assessing the district and proceeding with the facilities. The steps presented in the next sections are designed for implementation in a duo-level data audit approach. If implementing a single-level audit, Step 2 may optional depending on the nature of the problem being investigated. 10

19 Stage 1: Preparing for the Audit Preparations are done at the level commissioning the audit (district, province/national). To ensure transparency, selecting of data elements and facilities for inclusion during the audit should be done in a team and all programme managers responsible for the programmes whose data elements are targeted for audit should be present in that meeting. The preparatory stage is made up of five compulsory steps: 1 Selecting Data Elements One audit is rarely able to cover all the data elements from the service delivery and disease forms. Therefore, it is necessary to select data elements before the audit, while taking care to avoid biases arising from individual audit team member preferences. Guidelines have been provided on how to proceed with the selection so that each data element is given an equal chance of being included in the audit (refer to next page, Step 1 ) 2 Selecting Auditing Units and Forming Teams The administrative level commissioning an audit should receive in advance the list of facilities and districts that are targeted by the audit. This essential step is the precursor to all other preparations (logistics, review of relevant documentation and data, etc.) 3 Review Relevant Documents Documentation for review may include: Previous audit report(s) (if any), HMIS feedback reports/dispatch, Most recent performance assessment/technical support report, Annual/quarterly action plans, and Any other relevant information. Obtaining as much detail as possible about the targeted unit in advance may reduce the amount of time spent on site. 4 Packaging Information For Onsite Audit This step may be done concurrently with the review of documentation, depending on the diversity of skills in the audit team. Preparing data will include: Packaging hard copies of the aggregation forms; Retrieving the electronic copy of the dataset for the period and site under consideration; and Pre-populating the following assessment forms: Data Completeness, Collation Accuracy, Data Entry Accuracy and the Internal Consistency. 5 Notify The Target Audit Site Preparing for site visits includes drawing up a field schedule, forming teams and notifying the target sites. Each of these five steps is discussed in detail below. Step 1 Selecting Data Elements The audit team will use the following two Health Information Aggregation (HIA) frameworks to select data elements: Service Delivery Sampling Frame (HIA2), found in Annex 4, and Disease Sampling Frame (HIA1), found in Annex 5. Although it is desirable to select all the data elements on both HIA1 and HIA2 for inclusion in the audit, this practice proved to be highly inefficient in the earlier attempts at conducting audits due to the large number of data elements. For this reason, this document outlines procedures for selecting a 11

20 sample of data elements that can be included in the audit for a detailed review. The selection of data elements should be done as transparently as possible to avoid any levels of biasness. 1.1 Sampling Data Elements on the Service Delivery Aggregation Form (HIA2). The HIA2 has seven programme areas namely: Child Health and Nutrition; Reproductive Health and Family Planning; HIV/AIDS Services; General Curative Care; Medicines and Medical Supplies; Environmental Health; and Finance, supportive supervision and HMIS Quality Assurance. Using the September 2012 B ver. 2 HIA2 dataset, Table 4 summarises the number of reportable 4 data elements within each programme area from which the sample should be drawn for each session of the audit. Table 4: Sample Distribution Table for Data Elements on HIA2 Programme Area Total # of Data Elements Proportionate to the total Target Sample CL=95; CI=10 Ms Excel Formula for selecting each subsample # of times to refresh results [F9] (a) (b) (c) (d) (e) Child Health and Nutrition =RANDBETWEEN(1,7) 7 Reproductive Health- Antenatal and Postnatal =RANDBETWEEN(1,5) 4 Care Reproductive Health- Obstetrics =RANDBETWEEN(1,7) 6 Reproductive Health- Family Planning =RANDBETWEEN(1,3) 2 HIV Testing and Counselling =RANDBETWEEN(1,4) 3 PMTCT =RANDBETWEEN(1,8) 7 HIV Care and Treatment =RANDBETWEEN(1,12) 11 Voluntary Medical Male Circumcision =RANDBETWEEN(1,3) 2 Curative Care-OPD =RANDBETWEEN(1,7) 6 Cancer Screening and Diagnosis =RANDBETWEEN(1,1) 0 Curative Care- IPD =RANDBETWEEN(1,4) 3 Medicines and Supplies =RANDBETWEEN(1,4) 3 Environment Health =RANDBETWEEN(1,7) 6 Finance, supportive supervision and HMIS =RANDBETWEEN(1,1) 0 QA Total CL=Confidence Level; CI=Confidence Interval As shown in Table 4, there 323 reportable data elements for the seven selected programmes. The proportion to the total contributed by each programme is shown in column (b). The calculated minimum target sample from 323 at 95 per cent confidence level and 10 per cent margin of error is 73 data elements. This sample is distributed across the six programmes proportionate to size as shown in column (c). Columns (d) and (e) are meant to assist in picking the actual data elements from Annex 4 (see explanation below). To do so, Child Health and Nutrition (CHN) has been used as an example shown below: 4 These are data elements collected directly from source documents and not derived from other data summaries on the HIA2 calculated data elements are therefore excluded. 12

21 1.2 Open a blank Excel Spread sheet. a. Anywhere on the worksheet enter the formula: =RANDBETWEEN (1, 37). Where 1 stands for the bottom number and 37 for the upper number from which to pick a number randomly on the Child Health and Nutrition (CHN) data elements list in Annex 4. b. Copy or drag the formula to an extra eight cells to give you nine random numbers. Immediately copy the numbers that appear on your screen the first time you copy the formula. Delaying doing so may refresh the list. Your list may look like the sample in Table 5. Table 5: Example of Sampled Numbers - Child Health and Nutrition Programme Area Sampled Numbers Child Health and Nutrition 24, 15, 4, 37, 30, 1, 28, 20, 26 Reproductive Health- Antenatal & Postnatal Care Reproductive Health- Obstetrics Reproductive Health-Family Planning HIV Testing and Counselling PMTCT HIV Care and Treatment Voluntary Medical Male Circumcision Curative Care-OPD Cancer Screening and Diagnosis Curative Care- IPD Medicines and Supplies Environment Health Finances, Supportive Supervision and HMIS quality assurance 1.3 Use the sampling frame in Annex 4 to identify which data elements represent the sampled numbers in Table 5 from the CHN list. Proceed and produce a table as shown in the example in Table 6 (below). Table 6: Example - Matching Sampled Numbers with Data Elements Programme Area Sampled Numbers Corresponding Data Elements Child Health and Nutrition 24, 15, 4, 37, 30, 1, 28, 20, 26 Reproductive Health- Antenatal and Postnatal Care Reproductive Health- Obstetrics Reproductive Health-Family Planning HIV Testing and Counselling PMTCT HIV Care and Treatment Voluntary Medical Male Circumcision Curative Care-OPD Cancer Screening and Diagnosis CHN3-025, CHN2-060, CHN1-020, CHN3-090, CHN3-055, CHN1-005, CHN3-045, CHN3-005, CHN

22 Programme Area Sampled Numbers Corresponding Data Elements Curative Care- IPD Medicines and Supplies Environment Health Finances, Supportive Supervision and HMIS quality assurance 1.4 Repeat 1 and 2 above for each programme to select the desired sample size under each area as stipulated in column (d) of Table Sampling Data Elements on the Disease Aggregation Form (HIA1) The 2013 edition of the Disease Aggregation Form (HIA1) categorises diagnosis/conditions as follows: Notifiable diseases; Selected diseases; Other diseases; Obstetric complications; and Sexually Transmitted Diseases, Screening and Neonatal (Annex 5). Obstetric complications, screening and neonatal have been clustered into one category for purposes of generating a sampling list. Table 7: Sample Distribution Table for Data Elements on HIA1 Disease/Diagnosis Category # of Data Elements Proportionate to the total Target Sample CL=95;CI=10 Ms Excel Formula for selecting each subsample # of times to refresh results [F9] (a) (b) (c) (d) (e) Notifiable Diseases =RANDBETWEEN(1,14) 13 Selected Diseases =RANDBETWEEN(1,19) 18 Other Diseases =RANDBETWEEN(1,32) 31 Obstetric Complications 3 Sexually Transmitted Diseases 4 Total =RANDBETWEEN(1,6) 5 =RANDBETWEEN(1,8) 7 Note: Replicate instructions 1 to 3 under-sampling data elements on HIA2(Annex 4) and match the data elements with the sampling list in HIA1 (Annex 5). 14

23 Step 2 Selecting Data Audit Units and Forming Teams 2.1 Considerations in choosing sites Although all facilities are eligible for data audit, it is neither possible for the province to cover all the districts nor for the district to cover all facilities in one round of an audit. The level commissioning the audit should select sites to include in each round of the audit. Note: By the end of the year, each district and each facility should have undergone at least one data audit. In choosing health facilities to include in the audit, consider the following aspects: 1. Conduct a preliminary assessment of reporting completeness and timeliness for all the sites within the period under review. The auditing team may consider prioritising those sites that appear to be struggling so that a detailed review can be done through data auditing with the view of making improvements. 2. Review the previous audits conducted and the data improvement activity plans arising from those audits. The level commissioning the audit may choose to make follow ups on those sites that should have implemented remedial measures, so as to assess the progress. Note: Although prior knowledge of the quality of data can be a factor in choosing a site for inclusion in the audit; it should not be the only reason. The audit team may explore other factors to select sites. 2.2 What to do if a site does not provide some services If some of the sites selected do not provide services for the data elements selected in Step 1, you should create a special list for such facilities. For each facility that does not provide a service for the data element selected in Step 1: 1. List down all the data elements in question. 2. Replace the data elements by repeating the random selection. 3. Note down each number that appears for the first time and matches a service provided by this facility. Note: Since this is sampling with replacement, if a number that already has been selected or rejected laterreappears, then skip all such numbers and repeat the selection. 2.3 Who Should Be on The Auditing Team Before an audit can take place, the level commissioning the audit should ensure the following minimum requirements for team composition: Programme Evaluation (e.g., District Medical Officers or Planners); Programme Management (officers vested with protocols/guidelines for targeted services) Data Management (e.g., District/Senior Health Information Officers).This is the technical person who should facilitate the Data Quality Audit. Step 3 Review Relevant Documentation Among the documents that should be reviewed may include previous audit report (if any); HMIS feedback reports/dispatch; most recent performance assessment/technical support report; annual/quarterly action plans; and any relevant information hosted by individual programme managers that may not be in the listed documents. Obtaining as much detail as possible about the targeted unit may reduce the amount of time spent on site. 3.1 Previous audit reports If the target site has been audited before, it is advised that all outputs from the previous audit be reviewed. The review should focus on those areas that were identified as weak points and on which feedback was provided or remedial measures were implemented. It may be necessary to communicate 15

24 directly with the target site on those issues that were raised in previous audit if the audit team does not have any fresh information about what followed the assessment. 3.2 Feedback reports/dispatches If systems are functioning well, it is expected that before an audit is commissioned, all the data at each transmission level will have been processed for quality before transmission to the next administrative level. If there were any issues with the data, preliminary observations would have been made and communicated to the originating unit. Reviewing this documentation will alert the audit team on any pending issues on either ends of the correspondence. 3.3 Most recent PA/TSS report Performance Assessment (PA) and Technical Support Supervisions (TSS) are two supervisory systems that are institutionalised in the health sector at each administrative level. Although these activities are general in nature, their reports can bring out issues that may have direct impact on the quality of data. Such issues may include availability of staff, adequacy of infrastructure and issues with equipment, among others. Prior knowledge of this information will prepare the audit team on how to proceed with the audit when onsite. 3.4 Annual/Revised Action Plan The preparation of these guidelines is a step towards discouraging adhoc data audits that have been taking place over years. Results from past audits have not been translated into action in most instances. To domesticate recommendations arising from data audits, each level will be expected to plan and budget for data audits with a focus on remedying areas already identified through previous supervisory activities. Data Quality Audit should be taken as a routine exercise and therefore should be included in the Action Plan. 3.5 Additional Information from key informants The audit team should also seek out other reports produced by programme managers when they visit or meet with districts or facilities. These trip/event reports contain valuable information that would be useful to the audit. Step 4 Prepare Data for the Audit Once the data elements for the audit have been selected, the audit team should now prepare the data for audit. This MUST be done before the actual field work commences. Depending on which level is targeted for the audit, items to prepare beforehand may vary. 4.1 Auditing the Facility Level For each of the facilities to be audited, the District Medical Office should have the following information at hand before travelling to the field (Table 8). This will shorten the time spent with each facility. Table 8: Facility Audit - Things to Prepare # What to prepare 1 Copies of the most updated version of the Procedures and Indicators Manuals. 2 A record of when the facility submitted its reports during the past 3 months Unused printed (single leafs) or soft copies of registers, activity sheets, tally sheets and aggregation 3 forms. These copies should be of the same version as the ones facilities are expected to have been using during the period in reference. 4 Completed hardcopies of HIA1/2 submitted by the facility covering the last 12 months 5 Printed copy of data validation rules 6 Copies of any HMIS feedback between the district office and the facility 7 Copy of updated Action Plan 16

25 4.2 Auditing the District Level If the district health office is targeted for the audit, the auditors should prepare the items in Table before visiting the district. Table 9: District Audit - Things to Prepare What to prepare 1 Copies of the most updated version of the Procedures and Indicators Manuals. 2 DQA Guidelines 3 Approved District Action Plan for the current period 4 Copies of approved versions of the HIA1/2 for the period under review 5 Printed copy of data validation rules 6 Previous two Performance Assessment Reports 7 Copy of the database submitted by the district 8 A copy of correspondence to indicate data has been submitted (e.g. ) 9 Copies of any HMIS feedback between the district office and the facility Step 5 Prepare for Site Visits It is recommended that target health units for the audit are informed in advanced about the planned visit to the facility. However, if not well handled, this advance notice can threaten the overall outcome of the audit. Since the audit is meant to identify laxity so as to improve practice, target units should not be prepared to the point that they mask their daily practice thereby concealing their weaknesses. Sites targeted for the audit should be informed about the upcoming data audit at least a month before visiting the site. It is therefore recommended that the notice to the sites focuses on the following information: 5.1 Information for the District Medical Office The District Medical Officer should be present and guarantee accesses to his/her office. The officer in-charge of the HMIS and the DHIS should be present. Access to the computer hosting the DHIS database must be assured. Programme officers to be available for the audit. The DMO should make available all HMIS forms submitted by health facilities. Date and time the audit will take place. Anticipated duration of the audit. 5.2 Information for the Health Facility The in-charge of the facility should be present or delegate to relevant staff available. For the hospitals, the Director and the departmental or ward in-charges should equally be available for the audit. Representation from staff who complete registers, activity sheets, tally sheets. Availability of staff that are responsible for preparing the HIA1/2 Guaranteed access to patients cards, registers, activity sheets, tally sheets and where available the SmartCare database. Date and time the audit will take place. Anticipated duration of the audit. 17

26 Stage 2: Fieldwork - District Health Office Unlike at a facility, once notice has been sent to the District Medical Office and the proposed schedule is agreed, the audit team is likely to find the district team ready. Below is the proposed sequence of events: Step 6 Assess Data Management Structures and Systems 1- Meet with the District Medical Officer and explain what is expected and confirm the availability of key staff, materials and equipment. 2- Request for a meeting place (such as a conference room) where the audit team and the district team can hold the meeting. The audit team should proceed to administer the District Assessment Form(Annex 2) to the host in this order: a. Availability of trained staff to perform management functions (1A) b. Availability of Resources: Equipment, Materials and Space (1B) c. HMIS Feedback (1C) d. Knowledge of the HMIS Data Handling Deadlines (2.1A) e. Practice in meeting reporting deadlines (2.1B) f. Availability of completed HIA forms (2.2C) Note: It is important that all programme managers understand the implications of the questions. Step 7 Trace and Verify Data with Facility Reports Tracing and verification requires more time and concentration. These aspects of the data audit are covered by the following sections on the district data audit tool: Data Entry Completeness: Assess whether all reported data have been entered on the computer (2.2D). Data Entry Accuracy: Verifying whether aggregated data on HIA1/2 match the data on computer (2.3E). Internal data consistency: Whether expected relationships in the dataset are not violated (2.3F). Note: It is recommended that the Team Lead allocates two persons to complete the three sections. Assessing these sections should be done in collaboration with a selected team from the district office. After completing sections 2.2D, 2.3E and 2.3F, consolidate your finding and transfer the responses to the three corresponding Excel Sheets provided with the tool. Go over the preliminary findings with the district team, using Table 12: District Performance Assessment Guide (Step 10) as a guide for summarising your findings (also refer to Annex 7). 18

27 Stage 3: Fieldwork - Health Facility Once at the facility, the Team Lead should explain the objectives of the audit and how the exercise will benefit the facility, the patients and clients. It is a common occurrence that even if prior notice is given, health workers are always busy when visited. Do not disrupt normal patient care. If the registers and cards included in the review are currently in use, exclude those records until they are free - mostly in the afternoon. Although the order of tasks may vary from one facility to the other, below is the proposed sequence: Step 8 Assess Data Collection, Storage and Reporting Systems 1- Meet with the facility in-charge and explain the mission. Confirm whether the notice sent about this visit was received and if all the things requested have been prepared in readiness for the audit. 2- If there is shortage of staff and the in-charge cannot sit through the entire audit process, it is proposed that the Health Facility Assessment Form (Annex 1) be administered in this order of sections: a. Availability of trained staff to collect data from patients/clients during delivery of services (1A). b. Availability of reference and training materials (1B). c. HMIS Feedback (2C). d. Knowledge of the HMIS Data Handling Deadlines (2.1A). e. Practice in meeting reporting deadlines (2.1B). f. Availability of completed cards, registers, tally/activity sheets and HIA forms (2.2C). Step 9 Trace and Verify Data with Primary Data Sources 1- Once Step 8 has been completed, the audit team can request for space from which to go over sections 2.2D, 2.3E and 2.3D. These three sections demand more time and attentiveness. Note: To reduce on time, while sitting together, the audit team may decide to tackle the three sections simultaneously. Note down any observation that may require clarification with the facility in-charge. 2- After completing sections 2.2D, 2.3E and 2.3D: a. Consolidate and transfer responses onto the Excel Sheets (if computer is available), b. Complete Table 11: Facility Performance Assessment Guide (also refer to Annex 7), c. Go over the preliminary findings with the in-charge and his/her staff. 19

28 Stage 4: Finalise the Audit Step 10 Preliminary Dataa Analysiss and Presentation (Onsite) The two data instruments presented in Annexes 1 and 2 combine data collection and processing content onto the same from. Each instrument has 2 sections, and under each section there sub- that sections. Immediately after completing that section or subsection, there are summary statistics should be completed, which are found at the end of a subsection or the main section. Below is the sample description of each of these summaries, using Section 1A from the Facility Assessment Tool as an example: Table 10: Example on how to complete the questionnaires and score each section A-Availability of trained staff to collect data from patients/clients during delivery of services Read out each question carefully; depending on the response, make a circle around either 1 RESPONSE or 0 Yes No 101 Service Providers are responsible for opening and updating patient cards/files? Service Providers are responsible for completing activity/ /tally sheets? Clerks (if available) fill up registers/sheets from patient files at end of the day Have all the clinicians been trained in HMIS data collection procedures? Have all the staff members received training in the last 2 years Did all the training session last up to five days? 1 Sum of all circled responses [A]= Total Score Max=(1 x 6) [B]= 6 Sect1a=[A/Bx100] 4 =4/6 X 100 =66.7% Sum of All Circled Responses: This is the number of circles for the sub/section. Add the codes for the circled responses. From the example in Table 10, this would be: = 4. Total Score: Provides the denominator for calculating the score for the section. Count and sum the entries of all questions that have been responded to. Some questions may not apply to some healthh units (for example question # 103). In situations such as this, there will be no circle on either of the two responses and consequently, the entry will be omitted from the Total Score. Max is just a reminder that the Total Score cannot be larger than a maximum possible score for the section. Sect1a=[A/B x 100]: Is the percentage scored out of all the responses that apply. Note: The letters in the brackets []: A, B and C, are meant to uniquely identify each answer and vary from section to section. As discussed in Stages 2 and 3, at the end of dataa collection the audit team should present summary findings from the audit to the host unit, bearing in mind that detailed analysis will follow later with an assessment report. Preparing of summary findings can be done in two ways: Manual At the end of the interview, calculate summaries for each sub/section, copy, the results to either Table 11 or Table 12 (also refer to Annex 6) depending on whether it is a health facility or the district office that is being assessed. Discuss the performance of this facility with the facility staff, bearing in mind that these are preliminary results. Electronically If the assessment team has access to a computer, summary results in Tables 11 and 12 can be automatically computed using an automated Excel Spread sheet thatt has been preformatted and released with these guidelines. Once all the relevant questionss have been answered and entered onto the spread sheet, data similar to thatt requested through Tables 11 and 12, respectively, will be automatically presented in MS Excel Chart format. These charts are on the second worksheet labelled charts. Copy each chart to either a word processor or presentation software.

29 Regardless of whether manual or electronic processing is done, when presenting the findings, these can be phrased in terms of questions as shown in Tables 11 and 12 below. Since national targets have not yet been formulated, each district should set annual targets against which their sites should be assessed 1. Health Facility Table 11: Facility Performance Assessment Guide Intervention Statistic Label Guiding Questions Scored Targeted Needed? (Yes/No) Sect1a Are members of staff adequately trained to handle HMIS processes? Sect1b Are reference and data collection materials available? Sect3 Is the facility managing feedback properly Sect2b Are the basic data collection procedures (e.g. reporting timeliness) being adhered to, by referring to the standard documentation Sect2c Do the registers, tally/activity sheet have all the data elements needed for reporting? Sect2d Sect2e Sect2f 112 & Are the data across the data tools consistent from one step of data collection to the next - if not so, at what stage are the errors being introduced? Does this facility have correct quantities of stationery Is this facility using correct versions of the HMIS stationery? Overall Which components of the system require more attention? Use the spider chart 2. District Health Office Table 12: District Performance Assessment Guide Statistic Label Guiding Questions Scored Targeted Sect1a Is the district well-staffed to manage HMIS processes? Sect1b Is the equipment, materials and space adequate? Sect21Total Is the district management aware of reporting requirement? Sect2c Is the storage and filing of completed facility forms adequate? Sect2d Is the district up-to-date with entering data already submitted by health facilities? Is the district able to ensure that the data entered on Sect2e the computer is a true reflection of what the health facility submitted? Sect2f Does the district team demonstrate that they review the data from facilities before and after data entry Sect 3 Is the facility managing feedback properly Overall Which components of the system require more attention? Use the spider chart Intervention Needed? (Yes/No) 21

30 Note: Summaries generated through this process, will provide a basis for the main report. This table, together with the written narrative, are the two main products from this audit. Among other things, the narrative will discuss availability of stationery, human resources availability and skills, and possible recommendations arising from discussions with the facility staff(see Data Quality Improvement Plan, Step 13). Step 11 Preparation of the Audit Report Within one to two weeks of completing the audit in the last facility, the Audit Team should have produced the first draft Audit Report with all findings, conclusions and recommendations for improvements. The report should follow the proposed outline below: Section Contents I Executive Summary II Introduction and Background Purpose of the DQA Background on related HMIS efforts Data Elements and Reporting Period Rationale for selection Audit Sites Rationale for selection Description of the data-collection and reporting system (related to the data elements audited) III Assessment of the Determinant of Data Quality Description of the assessment steps for determinants of data quality Performance Table and Charts (from Excel Sheets) Discussion of findings Overall strengths and weaknesses of functional areas IV Assessment of Data Quality Description of the assessment steps for quality of data. Reporting Timeliness (sect2a, sect2b and sect21total) Reporting Completeness (sect2c, sect2d and sect22total) Data Accuracy (sect2e, sect2f and sect23total) V Conclusion and Recommendations Note: The report can include graphs (see Annex 5 for examples). 22

31 Step 12 Discuss Report and Formulate Recommendations For each question requiring investigation, describe the root causes of the problem, the action points, and intermediate target for improvement using the template in Table 13 as an example. Use one row for each question. You may need to use more than one sheet of paper where necessary. Table 13: Problem Analysis Matrix Question investigated Root causes Action points Intermediate target Step 13 Developa Data Quality Improvement Plan Table 13 will provide a guide on what needs to be done about an identified problem. However, not all problems can be fixed at the same time. Identified problems may fall into the following possible categories: Only local level interventions required: Some problems identified can easily be fixed without the involvement of any higher administrative levels. Examples may include streamlining the filing system, restocking facilities with right versions/quantities of materials, withdrawing old versions of tools from sites, etc. Some solutions may require short implementation time-spans while other may require more time and planning. All solution that demands time to implement should be included in either the district annual plan or the revised quarterly plans. Higher level interventions required: Other problems may not be resolved by the local level alone. For example, shortages of stationery require the intervention of the central administrative level. When such problems are identified, the normal channels of notifying the higher levels should be used. If the problem requires technical input other than funds, this should be reflected in the plans using existing planning procedures. 23

32 References Aqil, A., Lippeveld, T. &Hozumi, D. (2009) PRISM Framework: A Paradigm Shift for Designing, Strengthening and Evaluating Routine Health Information Systems. Health and Policy Planning 2000.[Online] 24(3), Available from: doi: /heapol/czp010 [Accessed 20 th August 2013]. Heeks, R. (2002) Information Systems and Developing Countries: Failure, Success and Local Improvisations. The Informatics Society. [Online] 18, Available from: doi: / [Accessed 29 th August 2013]. Lippeveld T, Sauerborn R, Bodart C Design and Implementation of Health Information Systems. Geneva: World Health Organization. Measure Evaluation Data Quality Audit Tool: Guidelines for Implementation. [Online].Available from: [Accessed 10 th August 2013]. Mutemwa, R.I. (2006) HMIS and Decision-Making in Zambia: Rethinking Information Solutions for District Health Management in Decentralised Health Systems. Journal of Health Policy and Planning [Online] 21(1), Available from: doi: /heapol/czj003 [Accessed 15 th August 2013]. Odhiambo-Otieno G.W. &Odero W.W.O. (2005) Evaluation Criteria For The District Health Management Information Systems: Lessons From The Ministry Of Health, Kenya. African Health Sciences [Online]5(1), Available from: [Accessed 21st August 2013]. UNAIDS (2008). Organizing Framework for a Functional National HIV Monitoring and Evaluation System. Geneva: UNAIDS. WHO. (2004) Developing Health Management Information Systems: A Practical Guide for Developing Countries. Switzerland: WHO Press. WHO. (1994) Information Support for New Public Action at the District Level. Geneva: World Health Organisation. Report of a WHO Expert Committee. [Online] 845, 1-31.Available from: [Accessed 26th August 2013]. 24

33 Annex 1: Facility Assessment Tool Routine Data Quality Audit Health Facility Assessment Form (HFA) District: Date: Facility: Assessor: Facility Type: Take note: After the completion of the soft copy of the HFA, graphs can be generated (as shown in Annex 5.) Section 1: Determinants of Data Quality A-Availability of trained staff to collect data from patients/clients during delivery of services Depending on the response, enter 1 if yes, 0 if no and "-" if not applying Response 101 Are service providers the only ones responsible for recording interactions on patient cards/files? Are service providers the ones responsible for completing activity/tally sheets? Clerks (if available) always fill up or update registers/sheets upon provision of a service by providers 1 0 Score 1 if all available staff have been trained else score 0 Exist Trained 104 How many health workers have been trained in HMIS data collection? How many members of staff received HMIS training/orientation in the last 1 year? How many days was longest training/orientation? SCORE 0 if < 3 days 1 0 Sum of all circled responses [A] = Total score (Max=1 x 6)[B] = Sect1a=[A/B x 100]= B-Availability of reference and data collection materials 107 Do you have a document that explains how to use the HMIS forms that are currently in use? What are the names of these documents? 1 0 a) HMIS Indicators Manual 1 0 b) HMIS Procedures Manual Ask to see the two documents. Were both documents provided easily without a search? 1 0 Compare the year and month each of these manuals were released Procedure Manual against the most up-to-date version. Are these the most recent versions? Indicators Manual 1 0 Intervention Area Number 111 Ask for copies of registers and activity sheets for the data elements covering period under review". "Are all the forms being used the correct versions? [To score 1, all the tools in the set must be up-todate. E.g., all registers for reproductive health must be up-to date]. a) Registers/Cards/any data source Checked Correct Child Health & Nutrition 1 0 Reproductive Health (ANC &PNC) 1 0 Reproductive Health-FP & Obstetrics 1 0 HIV Prevention, Treatment& Care 1 0 PMTCT 1 0 Voluntary Medical Male Circumcision 1 0 Curative: Outpatient 1 0 Medicines and Supplies 1 0 Environment Health 1 0 Curative : Inpatient

34 B-Availability of reference and data collection materials b) Activity /Tally Sheets Checked Correct Child Health and Nutrition 1 0 Reproductive Health (ANC & PNC) 1 0 Reproductive Health (FP & obstetrics) 1 0 HIV Prevention, Treatment &Care 1 0 PMTCT 1 0 Voluntary Medical Male Circumcision 1 0 Environment Health 1 0 Medicines and Supplies 1 0 Curative: Outpatient 1 0 Curative: Inpatient Have you run out of HMIS stationery at any point in the past six months? Do you have enough stock to last you the next six months Are all the HIA1/2 Forms under review bearing the name of who prepared the report Are all the HIA1/2 Forms under review bearing the name of who verified/approved 1 0 Sum of all circled responses [C] = Total Score(Max=1 x 32)[D] = Sect1b=[C/D x 100]= C-HMIS Feedback THIS SECTION SHOULD NOT BE ADMINISTERED BY THE DHIO The questions in this section are meant to assess the experience and practice concerning feedback on HMIS reports 116 In the last three months, have you received feedback on the data after submitting reports to the district? ASK FOR THE MOST RECENT COPY. Did the facility provide the report without searching? Is the feedback report kept in a dedicated folder/box file or filing cabinet? Sum of Entries [E] When feedback from the district requests to make changes to the data, what do you do? How do you communicate technical challenges to the district for support? a) Fills a new HIA (DESTROYS THE OLD COPY) 1 0 b) Fills a new HIA (KEEPS THE OLD COPY) 1 0 c) Fills a new HIA (CORRECTIONS ONLY) 1 0 d) Makes changes on the same copy 1 0 a) Written submission (SAMPLE EXISTS) 1 0 b) Written submission (NO SAMPLE EXISTS) 1 0 c) Verbally (SOURCE DOCUMENT EXISTS) 1 0 d) Verbally (NO SOURCE DOCUMENT EXISTS) 1 0 Total score(max=1 X 11)[F]= Sect1c=[E/F] x 100 = 26

35 Section 2: Data Quality Assessment 2.1 Reporting Timeliness A-Knowledge of the HMIS Data Handling Deadlines [Ask these questions to the In-charge] DO NOT PROMPT: If answer is correct make a circle around 1, otherwise circle on 0 After the month ends when should all the health centres send their reports to the district? (Answer = 7th day of the month has ended) SCORE 1 if response is correct) After the month has ended when should you receive feedback from the district? (Answer = Before the end of the second month) SCORE 1 if response is correct) When should the district send datasets to the province? (Answer = By the end of the second month) SCORE 1 if response is correct) When should the province submit the dataset to the national level? (Answer = By the fifth of the 3rd month) SCORE 1 if response is correct) RESPONSE Sum of all circled responses [G] = Total Score(Max=1 x 4)[H] = Sect2a=[G/H x 100]= B-Practice in meeting reporting deadlines 205a 205b Upon submission of monthly HMIS reports, health centres should have a record to show a form was received by the District Medical Office. Please ask to be shown proof that reports for the last three months (starting with most recent) were received by the district. If proof is provided, circle 1 otherwise circle on 0. Facilities should keep a record of when reports were submitted to the DMO. Verify if reports for the most recent months were submitted on time. If submitted on time, circle 1 otherwise circle on 0. Month Month Month Month Month Month Sum of all circled responses [I] = Total Score(Max=1 x 6)[J] = Sect2b=[I/J x 100]= 2.2. Report Completeness C-Availability of completed cards, registers, tally/activity sheets and HIA forms 206 Ask for filled copies of registers and activity sheets for the past six months [inclusive of the period under review] Are all the registers and activity sheets for the period available? [To score 1, all the tools in the set must be up-to-date. E.g., all registers for reproductive health must be up-to date]. Intervention Area Number RESPONSE a) Registers Expected Availed 1 0 Child Health and Nutrition 1 0 Reproductive Health- ANC and PNC 1 0 Reproductive Health- Obstetrics 1 0 Reproductive Health-Family Planning 1 0 HIV Testing and Counselling 1 0 PMTCT 1 0 HIV Care and Treatment 1 0 Voluntary Medical Male Circumcision 1 0 Curative Care-OPD 1 0 Medicines and Supplies 1 0 Environment Health 1 0 Finances,Supportive Supervision and HMIS quality assurance 1 0 Curative: Inpatient 1 0 b) Activity /Tally Sheets Expected Availed 27

36 C-Availability of completed cards, registers, tally/activity sheets and HIA forms 207 In the process of verifying the availability of data forms, please score 0 if you experienced any of the following, else score 1 Child Health and Nutrition 1 0 Reproductive Health- Antenatal and Postnatal Care 1 0 Reproductive Health- Obstetrics 1 0 Reproductive Health-Family Planning 1 0 HIV Testing and Counselling 1 0 PMTCT 1 0 HIV Care and Treatment 1 0 Voluntary Medical Male Circumcision 1 0 Curative Care- Outpatient 1 0 Medicines and Supplies 1 0 Environment Health 1 0 Finances,Supportive Supervision and HMIS quality assurance 1 0 Curative: Inpatient 1 0 Blank columns in registers/activity sheets 1 0 Missing pages/sheets for a month 1 0 Data for the whole month is missing 1 0 The whole register is missing 1 0 ASK FOR A BOX FILE OR THE BATCH WHERE HIA FORMS FOR THE PAST 12 MONTHS HAVE BEEN KEPT Please record "0" if you HAVE FOUND multiple copies of HIA forms for the same month, else "1" Count the months for which either the HIA1 or HIA2 are available. For each month the form is available, score 1 then sum the scores for the 12 months for each form. 1 0 # of months form is available HIA HIA 2 1 (0- (0-12) 12) Sum of ANSWERS [K] = Total score Max=(1 x 13)+(12 x 2) [L] = Sect2c=[K/L x 100] = D-Data Completeness: Assess whether all required data elements have been entered on the form 210 USE THE MOST RECENT HIA1 AND HIA2 See section on "Selecting Data Elements" to understand the numbers in brackets For each data element selected, score 1 if the respective cell on the form has a value recorded. Sum all the 1 s for each intervention area. Intervention Area Child Health and Nutrition Reproductive Health- Antenatal and Postnatal Care Reproductive Health- Obstetrics Reproductive Health-Family Planning HIV Testing and Counselling PMTCT HIV Care and Treatment # of data elements filled HIA HIA

37 D-Data Completeness: Assess whether all required data elements have been entered on the form Sum of Entries [M] = Total score Max=(106)[N]= Voluntary Medical Male Circumcision Curative Care-OPD Medicines and Supplies Environment Health Finances,Supportive Supervision and HMIS quality assurance Diagnoses and Deaths (0-44) Sect2d=[M/N] x 100 = E-Collation Accuracy: Verifying whether aggregated data on HIA1/2 match the primary data source 211 USE THE MOST RECENT HIA1 AND HIA2 See section on "Selecting Data Elements" to understand the numbers in brackets For each data element selected, score 1 if the respective cell on the form matches the entry on the intermediary summary form (Register, Activity/Tally Sheets. Sum of Entries [O] = Total score Max=(298)[P]= Intervention Area Child Health and Nutrition Reproductive Health- Antenatal and Postnatal Care Reproductive Health- Obstetrics Reproductive Health-Family Planning HIV Testing and Counselling PMTCT HIV Care and Treatment Voluntary Medical Male Circumcision Curative Care-OPD Medicines and Supplies Environment Health Finances,Supportive Supervision and HMIS quality assurance Diagnoses and Deaths (0-44) Sect2e=[O/P] x 100 = # of matching data elements HIA HIA 2 1 F-Internal Data Consistency: Whether expected relationships in the dataset are not violated DON'T USE DATA FROM THE DHIS DATABASE TO ANSWER THIS QUESTION. USE HARD COPIES 212 For the list of Validation Rules use Annex XX, Under each intervention area, randomly select five (5) validation rules. Validate the most recent reports on the selected rules, score 1 if the rule is NOT violated. Sum all the 1 s for each intervention area Intervention Area Child Health and Nutrition Reproductive Health- Antenatal and Postnatal Care Reproductive Health- Obstetrics Reproductive Health-Family Planning HIV Testing and Counselling # of rules passed HIA HIA

38 F-Internal Data Consistency: Whether expected relationships in the dataset are not violated Sum of Entries [Q] = PMTCT Total score Max=(5x5)[R]= HIV Care and Treatment Voluntary Medical Male Circumcision Curative Care-OPD Medicines and Supplies Environment Health Finances,Supportive Supervision and HMIS Quality Assurance Diagnoses and Deaths (0-44) Sect2f=[Q/R] x 100 = 30

39 Facility HMIS Data Quality Audit Data Completeness Assessment Form District Name:... Facility Name:... Reviewer.. Period: Start Month to End Month Year:.. For each data element included in this assessment, review the HIA1/2 for the months being audited. If a data element is not on the form that was sent to the district, for that data element, record 0 against that month, if it was reported, enter 1. NOTE: Use a full sheet if reviewing many elements or months. Data Element (ID) Months Under Review 1 Insert 2 Insert 3 Insert 4 Insert 5 Insert 6 Insert 7 Insert 8 Insert 9 Insert 10 Insert 11 Insert 12 Insert Count of Months Reviewed With Data 31

40 Facility HMIS Data Quality Audit Collation Accuracy Assessment Form District Name:...Facility Name:... Month: Year:.. Please recount these numbers (under review) from the source documents and compare with the hard copy that you collected from the district office. This can be done in this order: OPD/IPD Register Tally sheets HIA1; Registers/Activity sheets HIA2. Note: Use a different sheet for each month reviewed. Data Element (ID) # on HIA1 or HIA2 form # found on the.. Amount of variance Register Tally/Activity On Register Tally/Activity 32

41 Facility HMIS Data Quality Audit Internal Consistence Assessment Form District Name:... Facility Name:...Reviewer:.. Period: Start Month to End Month Year:.. Once you have selected the validation rules for each intervention area, review the selected data elements from the HIA1/2 for the month(s) being audited. If a data element violates a rule, recode 0, otherwise enter 1. NOTE: Use a more sheets if reviewing many elements or months. # Intervention Area Selected Validation Rule 1 Child Health and Nutrition 2 Child Health and Nutrition 3 Child Health and Nutrition 4 Child Health and Nutrition 5 Child Health and Nutrition 6 Reproductive Health 7 Reproductive Health 8 Reproductive Health 9 Reproductive Health 10 Reproductive Health 11 HIV Prevention, CareandTreatment 12 HIV Prevention, CareandTreatment 13 HIV Prevention, CareandTreatment 14 HIV Prevention, CareandTreatment 15 HIV Prevention, CareandTreatment 16 Curative: Outpatient/Inpatient 17 Curative: Outpatient/Inpatient 18 Curative: Outpatient/Inpatient 19 Curative: Outpatient/Inpatient 20 Curative: Outpatient/Inpatient Basic Data Element ID Total Score Total Score Total Score Total Score Has the rule been passed? Yes=1; No=0 33

42 Annex 2: District Assessment Tool Routine Data Quality Audit District Assessment Form Province District Visit Type PA/TSS/DQA? Date Assessor: Section 1: Determinants of Data Quality A-Availability of trained staff to perform management functions Read out each question carefully; depending on the response, make a circle around either 1 or 0 Response Yes No 101 Is there a specific officer designated to receive HMIS monthly reports? (SKIP TO 103 IF 101=0) Which officer receives the monthly reports (Code the designation of the officer receiving the reports) Is there an officer dedicated to record the health facility reports received (SKIP TO 105 IF 103=0) Are the reports indicated in Q103 above, verified before entry? (MoV = Check FEEBACK Report) Does the District Medical Office approve the reports before submitting them to the PMO? (i.e. DHIS 2) 1 0 How many programme managers have been oriented in the use of DHIS 2 All Oriented database? (CIRCLE 1 IF ALL HAVE BEEN ORIENTED OR ELSE 0 ) Are there meetings held to look at the performance of health facilities (SKIP TO 110 IF 107=0) How often are these meetings held? (NOTE: Tick one that applies) (IF HELD QUARTERLY, CIRCLE 1 OR ELSE CIRCLE 0 ) Quarterly Monthly Annually Is there evidence of such meetings? (IF 1 CHECK FOR THE MINUTES) IF 109= 0 SKIP TO NEXT SECTION 110 Are there any data audits activities undertaken to ensure quality of HMIS data? IF YES CHECK FOR THE DATA AUDIT REPORTS, OR ELSE SKIP TO THE NEXT SECTION) Total Sum of all circled responses [A] = score(max=1 x Sect1a=[A/B x 100]= 12)[B] = B-Availability of reference and data collection materials Check availability of these documents that explains how to manage the HMIS (CIRCLE 1 IF AVAILABLE OR SKIP TO 114 IF ALL RESPONSES ARE 0 ) Check if these documents that explains how to manage the HMIS are available in print (CIRCLE 1 IF IN PRINT OR ELSE CIRCLE 0 ) 113 Is the district using the most up-to-date version of these manuals? HMIS Indicators Manual 1 0 HMIS Procedures Manual 1 0 DHIS Manual 1 0 DQA Guidelines 1 0 HMIS Indicators Manual 1 0 HMIS Procedures Manual 1 0 DHIS Manual 1 0 DQA Guidelines 1 0 HMIS Indicators Manual 1 0 HMIS Procedures Manual 1 0 DHIS Manual 1 0 DQA Guidelines Is there a computer assigned for data entry

43 B-Availability of reference and data collection materials 115 Does the district have a dedicated computer for keeping copies of DHIS data mart when internet connectivity is unavailable? (IF 1 CHECK FOR THE BACKED UP DATA) Is there a filing system for the health facility reports HIA reports (IF 1 CHECK FOR THE FILLING SYSTEM) Do you know how long the available stock of the HMIS data collection tools will last? Are these the most recent versions of the HMIS data collection tools? (IF 1 CHECK FOR THE VERSION) Do you have reliable internet connectivity?(skip TO THE NEXT SECTION IF 121=0) Is there a budget line to support internet connectivity at the district? 1 0 Sum of all circled responses [C] = Total Score(Max=1 x 19)[D] = Sect1b=[C/D x 100]= C-HMIS Feedback The questions in this section are meant to assess the experience and practice concerning feedback on HMIS reports 121 In the last three months, have you received feedback on the data after submitting reports to the province? ASK FOR THE MOST RECENT COPY. Did the district provide the report without searching? Is the feedback report kept in a dedicated folder/box file or filing cabinet? 1 0 a) Start by comparing the new submission with the 1 0 When feedback from the province requests previous one. changes to the data, you have relayed the b) Re-enters the whole form (BACKS UP THE OLD information to the affected facility and DATAFILE) corrections are sent to you. What do you do? c) Corrects only the data fields that had errors Sum of Entries [E] How do you communicate HMIS technical challenges to the province for support? a) Written submission (SAMPLE EXISTS) 1 0 b) Written submission (NO SAMPLE EXISTS) 1 0 c) Verbally (SOURCE DOCUMENT EXISTS) 1 0 d) Verbally (NO SOURCE DOCUMENT EXISTS) 1 0 Total score(max=1 X 10)[F]= Sect1c=[E/F] x 100 = Section 2: Data Quality Assessment 2.1. Reporting Timeliness A-Knowledge of the HMIS Data Handling Deadlines [Ask these questions to the DMO] DO NOT PROMPT: If answer is correct make a circle around 1, otherwise circle on 0 Yes No After the month ends when should all the health centres send their reports to the district? (Answer = 7th day of the month has ended) SCORE 1 if response is correct) After the month has ended when should you receive feedback from the district? (Answer = Before the end of the second month) SCORE 1 if response is correct) When should the district send datasets to the province? (Answer = By the end of the second month) SCORE 1 if response is correct) When should the province submit the dataset to the national level? 204 (Answer = By the fifth of the 3rd month) SCORE 1 if response is correct) Total Sum of all circled responses [G] = Score(Max=1 x 4)[H] = Sect2a=[G/H x 100]=

44 B-Practice in meeting reporting deadlines 205a 205b Upon submission of monthly HMIS reports, the District Medical Office should have a record to show a form was submitted by health facilities. Please ask to be shown proof that reports for the last three months (starting with most recent) were submitted to the district. If proof is provided, circle 1 otherwise circle on 0. Please check the record for the past three months. Enter number of expected reports, and the number reported on time. If all facilities reported on time, Score 1 otherwise enter 0. Sum of all circled responses [I] = 2.2. Report Completeness Total Score(Max=1 x 6)[J] = Month Month Month All On Time Score Reports Month Month Month Sect2b=[I/J x 100]= C-Availability of completed HIA forms Please record "0" if you HAVE FOUND multiple copies of HIA forms for the same month, else 206 "1" 207 RANDOMLY SELECT FIVE (5) FACILITIES. ASK FOR A BOX FILE OR THE BATCH WHERE HIA FORMS FOR THE PAST 12 MONTHS FOR THE FIVE FACILITIES HAVE BEEN KEPT. Count the months for which either the HIA1 or HIA2 are available. For each month the form is available, score 1 then sum the scores for the 12 months for each form for all (5) facilities. 1 0 # of months form is available HIA 2 HIA 2 (0-60) (0-60) Sum of ANSWERS [K] = 0 Total score Max=(1)+(60x2) [L] = Sect2c=[K/L x 100] = - D-Data Entry Completeness: Assess whether all reported data have been entered on the computer 208 Sum of Entries [M] = USE THE MOST RECENT HIA1 AND HIA2 reports. Firstly sample two (2) health facilities and in each, sample the required number of data elements per intervention. Compare data on hard copy with the data on DHIS. Score 1 if paper and electronic copies both have entries, then sum all the 1 s for each intervention area (both facilities) NOTE: the maximum is the total sampled data elements X2 per intervention Total score Max=(106)[N]= Intervention Area Child Health and Nutrition Reproductive Health- Antenatal and Postnatal Care Reproductive Health- Obstetrics Reproductive Health-Family Planning HIV Testing and Counselling PMTCT HIV Care and Treatment Voluntary Medical Male Circumcision Curative Care-OPD Medicines and Supplies Environment Health Finances,Supportive Supervision and HMIS Quality Assurance Diagnosis and Deaths [0-88] Sect2d=[M/N] x 100 = # of data elements filled HIA 2 HIA 2 36

45 2.3 Data Accuracy E-Data Entry Accuracy: Verifying whether aggregated data on HIA1/2 match the data on computer 209 Sum of Entries [O] = USE THE MOST RECENT HIA1 AND HIA2 reports. Firstly sample two (2) health facilities and in each, sample the required number of data elements per intervention. For each data element selected, score 1 if the respective cell on the form matches the entry on the computer. Sum the 1s for both facilities NOTE: the maximum is the total sampled data elements X2 per intervention Total score Max=(298)[P]= Intervention Area Child Health and Nutrition Reproductive Health- Antenatal and Postnatal Care Reproductive Health- Obstetrics Reproductive Health-Family Planning HIV Testing and Counselling PMTCT HIV Care and Treatment Voluntary Medical Male Circumcision Curative Care-OPD Medicines and Supplies Environment Health Finances,Supportive Supervision and HMIS quality assurance Diagnosis and Deaths [0-88] # of matching data elements HIA 2 Sect2e=[O/P] x 100 = - HIA 2 F-Internal Data Consistency: Whether expected relationships in the dataset are not violated 210 DON'T USE DATA FROM THE DHIS DATABASE FOR THIS QUESTION. USE HARD COPIES For the list of Validation Rules use Annex 3, Under each intervention area, randomly select five two facilities and validation rules. Validate the most recent reports on the selected rules, score 1 if the rule is NOT violated. Sum all the 1 s for each intervention area Sum of Entries [Q] = 0 Total score Max=(10X5)[R]= Intervention Area Child Health and Nutrition [0-5] Reproductive Health- Antenatal and Postnatal Care Reproductive Health- Obstetrics Reproductive Health-Family Planning HIV Testing and Counselling PMTCT HIV Care and Treatment Voluntary Medical Male Circumcision Curative Care-OPD Medicines and Supplies Environment Health Finances,Supportive Supervision and HMIS Quality Assurance Diagnosis and Deaths [0-88] # of rules passed HIA HIA Sect2f=[Q/R] x 100 =

46 Data Accuracy E-Data Entry Accuracy: Verifying whether aggregated data on HIA1/2 match the data on computer USE THE MOST RECENT HIA1 AND HIA2 TO COMPLETE THE QUESTION. # of matching data elements 209 USE THE MOST RECENT HIA1 AND HIA2 Reports Firstly sample two (2) health facilities and in each, sample the required number of data elements per intervention. Compare data on hard copy with the data on DHIS. Score 1 if paper and electronic copies both have entries, then sum all the 1 s for each intervention area for both facilities. Note: You can use Data Entry Accuracy Assessment Form in the Annex for additional analysis. Intervention Area HIA 1 HIA 2 Child Health and Nutrition (0-18) Reproductive Health(0-32) HIV Prevention, Treatment and Care(0-58) Curative: Outpatient/Inpatient [0-16] Diagnosis and Deaths [0-88] Sum of Entries [O] = Total score-max(106) [P] = Sect2e=[O/P] x 100 = F-Internal data consistency: Whether expected relationships in the dataset are not violated USE DATA FROM THE DHIS DATABASE TO ANSWER THIS QUESTION. DO NOT USE HARD COPIES 210 For the list of Validation Rules use Annex XX, Under each intervention area, randomly select two facilities and five validation rules. Validate the most recent reports on the selected rules, score 1 if the rule is NOT violated. Sum all the 1 s for each intervention area # of rules passed Intervention Area HIA 1 HIA 2 Child Health and Nutrition [0-10] Reproductive Health [0-10] HIV Prevention, Treatment and Care [0-10] Curative: Outpatient/Inpatient [0-16] Diagnosis and Deaths [0-88] Sum of Entries [Q] = Total score-max(10 X 5) [R] = Sect2f=[Q/R] x 100 = O + R = P + R = Sect23Total=[O+R]/[P+R] x 100 = 38

47 District HMIS Data Quality Audit Data Completeness Assessment Form District Name:... Facility Name:... Reviewer.. Period: Start Month to End Month Year:.. For each data element included in this assessment, review the HIA1/2 for the months being audited. If a data element is not on the form that was sent to the district, for that data element, record 0 against that month, if it was reported, enter 1. NOTE: Use a full sheet if reviewing many elements or months. Data Element (ID) Months Under Review 1 Insert 2 Insert 3 Insert 4 Insert 5 Insert 6 Insert 7 Insert 8 Insert 9 Insert 10 Insert 11 Insert 12 Insert Count of Months Reviewed With Data 39

48 District HMIS Data Quality Audit Data Entry Accuracy Assessment Form District Name:...Facility Name:... Period: Start Month to End Month Year:. Please compare the number on the report form (HIA1/2) that was used for entering data for the month under review. If numbers do not match, copy the number on the form onto the column labelled # on HIA for the respective month and the number on the DHIS on the respective column labelled # on DHIS. Where both numbers match, simply put a dash (----). In the summary columns, count the number of months with matching numbers and record in # of matches. Count the number of months reviewed and enter under # of months Data Element (ID) Month 1 [ Insert ] Month 2 [ Insert ] Month 3 [ Insert ] Month 4 [ Insert ] Month 5 [ Insert ] Month 6 [ Insert ] # on HIA # On DHIS # on HIA # On DHIS # on HIA # On DHIS # on HIA # On DHIS # on HIA # On DHIS # on HIA # On DHIS Summary # of matches # of months Per cent match 40

49 District HMIS Data Quality Audit Internal Consistence Assessment Form District Name:... Facility Name:...Reviewer:.. Period: Start Month to End Month Year:.. Once you have selected the validation rules for each intervention area, review the selected data elements from the HIA1/2 for the month(s) being audited. If a data element violates a rule, recode 0, otherwise enter 1. NOTE: Use a more sheets if reviewing many elements or months. # Intervention Area Selected Validation Rule 1 Child Health and Nutrition 2 Child Health and Nutrition 3 Child Health and Nutrition 4 Child Health and Nutrition 5 Child Health and Nutrition 6 Reproductive Health 7 Reproductive Health 8 Reproductive Health 9 Reproductive Health 10 Reproductive Health 11 HIV Prevention, CareandTreatment 12 HIV Prevention, CareandTreatment 13 HIV Prevention, CareandTreatment 14 HIV Prevention, CareandTreatment 15 HIV Prevention, CareandTreatment 16 Curative: Outpatient/Inpatient 17 Curative: Outpatient/Inpatient 18 Curative: Outpatient/Inpatient 19 Curative: Outpatient/Inpatient 20 Curative: Outpatient/Inpatient Basic Data Element ID Total Score Total Score Total Score Total Score Has the rule been passed? Yes=1; No=0 41

50 Annex 3: Service Deliver Sampling Frame HIA2 Programme Area And Data Elements Random Number 1 Child Health & Nutrition (CHN) 1.1 Under 5 Clinic Attendance CHN1-005 Attendance child health <12 months male 1 CHN1-010 Attendance child health <12 months female 2 CHN1-015 Attendance child health months male 3 CHN1-020 Attendance child health months female 4 CHN1-030 Attendance from outside catchment s area Growth monitoring and nutrition CHN2-005 Child 0 23 months weighed 6 CHN2-010 Child months weighed 7 CHN2-020 Not gaining weight 0 23 months 8 CHN2-025 Not gaining weight months 9 CHN2-035 Weight between -2Z & -3Z scores 0 23 months 10 CHN2-040 Weight between -2Z & -3Z scores months 11 CHN2-045 Weight below -3Z scores 0 23 months 12 CHN2-050 Weight below -3Z scores months 13 CHN2-055 Weight above +2Z scores 0 23 months 14 CHN2-060 Weight above +2Z scores months 15 CHN2-065 Vitamin A supplement to 6-11 months infant 16 CHN2-070 Vitamin A supplement to months child 17 CHN2-075 De worming dose to child months 18 CHN2-080 Children who received insecticide-treated nets (ITNs) Immunisation CHN3-005 BCG dose (<1 Year) 20 CHN3-010 OPV 0 (<1 Year) 21 CHN3-015 OPV 1 (<1 Year) 22 CHN3-020 OPV 2 (<1 Year) 23 CHN3-025 OPV 3 (<1 Year) 24 CHN3-030 OPV 4 (<1 Year) 25 CHN3-035 DPT-Hib+HepB 1 (<1 Year) 26 CHN3-040 DPT-Hib+HepB 2 (<1 Year) 27 CHN3-045 DPT-Hib+HepB 3 (<1 Year) 28 CHN3-050 PCV 1 (<1 Year) 29 CHN3-055 PCV 2 (<1 Year) 30 CHN3-060 PCV 3 (<1 Year) 31 CHN3-065 RV 1 (<1 Year) 32 CHN3-070 RV 2 (<1 Year) 33 CHN3-075 Measles 1st dose (<1 Year) 34 CHN3-080 Fully Immunised (<1 Year) 35 CHN3-085 Measles 2nd dose at 18 months 36 CHN3-090 Number of days fridge non-functional Reproductive Health Services (IRH) 2.1 Antenatal First antenatal visit IRH1-005 Antenatal 1st visit before 14 weeks 1 IRH1-010 Antenatal 1st visit 14 to 19 weeks 2 42

51 Programme Area And Data Elements Random Number IRH1-020 Antenatal 1st visit 20 weeks or later 3 IRH1-030 Antenatal 1st visit by woman < age of 18 years Antenatal Follow-ups IRH1-035 Antenatal follow-up visits 5 IRH1-045 At least 4 ANC visits 6 IRH1-050 Antenatal attendance from outside catchment s area Screening IRH1-055 Screened for anaemia at first ANC visit 8 IRH1-060 Antenatal client tested for syphilis 9 IRH1-065 Antenatal client tested positive for syphilis Prophylaxis during pregnancy IRH1-070 IPT 1 11 IRH1-075 IPT 2 12 IRH1-080 IPT 3 13 IRH1-085 ITN provided at ANC visit 14 IRH1-090 Deworming dose 15 IRH1-095 Ferrous Sulphate dose 16 IRH1-100 Folic acid dose 17 IRH1-105 Pregnancies protected by TT Postnatal IRH2-005 Postnatal care within 6 days of delivery 19 IRH2-010 Postnatal care between 6 days - 6 weeks 20 IRH2-120 Vitamin. A supplement to woman within 8 wks after delivery Family Planning Attendances IRH3-005 Attendance family planning (New acceptors) 1 IRH3-010 Attendance family planning -Revisit 2 IRH3-015 Attendance family planning - other Methods IRH3-025 Male Condoms (# of pieces issued) 4 IRH3-030 Female Condoms (# of pieces issued) 5 IRH3-035 Combined Oral contraceptives (# of cycles issued) 6 IRH3-040 Progesterone only pill (# of cycles issued) 7 IRH3-045 Medroxyprogesterone injection 8 IRH3-050 Norethisterone enanthate injection 9 IRH3-055 Implant 10 IRH3-060 IUCD inserted 11 IRH3-065 Sterilisation female 12 IRH3-070 Sterilisation - male Obstetric Care Deliveries IRH4-005 Normal deliveries in facility 1 IRH4-010 Assisted delivery in facility (Vacuum/Forceps) 2 IRH4-015 Caesarean section Delivery Supervision IRH4-025 Deliveries by skilled personnel (Midwife/Obstetrician) 4 IRH4-030 Deliveries by other skilled personnel(nurses, CO, ML, MO) 5 IRH4-040 Deliveries by other facility staff 6 IRH4-045 Deliveries in facility by trained TBA's 7 IRH4-055 Home deliveries by trained TBA's 8 43

52 Programme Area And Data Elements Random Number IRH4-060 Home deliveries by any TBA's Pregnancy / Delivery Complications IRH4-065 Sepsis 10 IRH4-070 Obstructed labour 11 IRH4-075 Hypertensive disorders 12 IRH4-080 Haemorrhage 13 IRH4-085 Abortion 14 IRH4-090 Ruptured uterus 15 IRH4-095 Retained placenta 16 IRH4-100 Obstetric Fistula 17 IRH4-110 Maternal deaths in facility Procedures Performed IRH4-115 Manual vacuum Aspiration for incomplete abortion 19 IRH4-120 Manual removal of placenta 20 IRH4-125 Magnesiumsulphate given for Pre-Eclampsia / Eclampsia 21 IRH4-130 IV antibiotics given for sepsis Outcome of Delivery Live Births IRH5-005 Live birth in facility <2500g 23 IRH5-010 Live birth in facility >=2500g 24 IRH5-020 Baby initiated to breast feed within an hour of birth Still Births IRH5-025 Macerated still birth in facility 26 IRH5-030 Fresh still birth in facility 27 IRH5-040 Asphyxia Neonatal Deaths IRH5-045 Inpatient death 0 to 7days early neonatal 29 IRH5-050 Inpatient death 8 to 28 days late neonatal 31 3 HIV/AIDS Services (HIV) 3.1 Counselling and Testing (Excluding PMTCT) Testing 1 HIV1-005 Males (<15) 2 HIV1-010 Females (<15) 3 HIV1-015 Males (15+) 4 HIV1-020 Females (15+) 5 HIV1-030 Individuals (only) 6 HIV1-035 Couples (only) HIV Positive Results HIV1-040 Males (<15) 8 HIV1-045 Females (<15) 9 HIV1-050 Males (15+) 10 HIV1-055 Females (15+) 11 HIV1-065 Concordant Couples (HTC) 12 HIV1-070 Discordant Couples (HTC) Receiving Results HIV1-075 Males Pos (<15) 14 HIV1-080 Females Pos (<15) 15 HIV1-085 Males Pos (15+) 16 HIV1-090 Females Pos (15+) 17 HIV1-100 Total received negative results 18 44

53 Programme Area And Data Elements HIV1-110 Referred from HCT to ART Prevention of Mother-to-Child Transmission of HIV Counselling& Testing HIV2-005 Tested - 1st ANC Visit 1 HIV2-010 Tested - Subsequent ANC Visit 2 HIV2-020 Labour& Delivery 3 HIV2-025 Postnatal within 72 hours of delivery 4 HIV2-035 Collecting Positive result 5 HIV2-040 Collecting Results - (pos&neg) HIV Positive Result 7 HIV2-045 Known positive status at 1st ANC Visit 8 HIV2-050 During Antenatal 9 HIV2-055 Labour& Delivery 10 HIV2-060 Postnatal within 72 hours of Delivery Partner Involvement 12 HIV2-075 Male partners tested - (ANC/L&D) 13 HIV2-080 Discordant Couples (ANC) Post-test Services 15 HIV2-085 Assessed - WHO Clinical staging (only) 16 HIV2-090 Assessed - CD4 Count 17 HIV2-100 Eligible for ART 18 HIV2-105 Started on ART in ANC 19 HIV2-110 Referred for HIV Care 20 HIV2-115 Screened for TB Maternal Prophylaxis(first issues only) HIV2-120 NVP 22 HIV2-125 AZT 23 HIV2-130 On ART (Includes HIV2-105) Infant Prophylaxis (first issues only) HIV2-140 NVP at birth or soon after birth HIV Exposed Infant Net Cohorts HIV3-005 Number of HEI at 2 months 26 HIV3-010 Number of HEI at 6 months 27 HIV3-015 Number of HEI at 12 months Infant Feeding HIV3-020 BF (12 months) 29 HIV3-025 Not BF (12 months) 30 HIV3-030 Not Known Infant Testing (Initial tests only) HIV3-040 Virology (within 2 months) 32 HIV3-045 Virology (from 3 to 11 months) 33 HIV3-050 Serology antibody test (at 12 months) 34 HIV3-055 Virology (at 12 months) Confirmed Infant Test Results HIV3-065 Positive (within 2 months) Virology 36 HIV3-070 Positive (from 3 to 11 months) Virology 37 HIV3-075 Positive (at 12 months) Virology Care and treatment Registering for Pre ART (entry points) HIV4-005 Pre ART registration from Counselling and Testing 1 Random Number 45

54 Programme Area And Data Elements HIV4-010 Pre ART registration from PMTCT 2 HIV4-015 Pre ART registration from TB 3 HIV4-020 Pre ART registration from other sources Cotrimoxazole Prophylaxis HIV4-030 HIV-Exposed Infant on Cotrimoxazole (within 2 months) 5 HIV4-035 HIV-Exposed Infants (reached 2 months) 6 HIV4-040 On CTX (<1) 7 HIV4-045 On CTX Male (1-4) 8 HIV4-050 On CTX Female (1-4) 9 HIV4-055 On CTX Male (5-14) 10 HIV4-060 On CTX Female (5-14) 11 HIV4-065 On CTX Male (15+) 12 HIV4-070 On CTX Female (15+) Enrolment in HIV Care HIV4-080 Enrolled in Care (<1) 14 HIV4-085 Enrolled in Care Male (<15) 15 HIV4-090 Enrolled in Care Female (<15) 16 HIV4-095 Enrolled in Care Male (15+) 17 HIV4-100 Enrolled in Care Female (15+) Currently in HIV Care HIV4-140 Currently in Care (<1) 19 HIV4-145 Currently in Care Male (<15) 20 HIV4-150 Currently in Care Female (<15) 21 HIV4-155 Currently in Care Male (15+) 22 HIV4-160 Currently in Care Female (15+) Eligibility for ART HIV4-170 Patients eligible for ART this month Starting ART HIV4-175 Start ART (<1) 25 HIV4-180 Start ART Male (<15) 26 HIV4-185 Start ART Female (<15) 27 HIV4-190 Start ART Male (15+) 28 HIV4-195 Start ART Female (15+) 29 HIV4-205 Start ART Pregnant 30 HIV4-210 Start ART - TB Currently on ART HIV4-245 Current on ART (<1) 32 HIV4-250 Current on Male (<15) 33 HIV4-255 Current on Female (<15) 34 HIV4-260 Current on Male (15+) 35 HIV4-265 Current on Female (15+) Cumulative Ever on ART HIV4-275 Ever Started ART Male (<15) 37 HIV4-280 Ever Started ART Female (<15) 38 HIV4-285 Ever Started ART Male (15+) 39 HIV4-290 Ever Started ART Female (15+) Retention on ART HIV4-300 On Original 1st Line at 12 months 41 HIV4-310 On alternative 1st Line at 12 months 42 HIV4-320 On 2nd Line (or higher) at 12 months 43 Random Number 46

55 Programme Area And Data Elements Random Number HIV4-340 ART Net Cohort at 12 months Screening in HIV HIV4-345 Screened for TB 46 HIV4-350 Screened for cervical cancer Post Exposure Prophylaxis Type of Client 48 HIV5-005 Occupational 49 HIV5-010 Sexual Assault 50 HIV5-015 Others PEP Provided HIV5-025 Occupational 56 HIV5-030 Sexual Assault 57 HIV5-035 Others Voluntary Medical Male Circumcision Number Circumcised HIV to 11 months 1 HIV to 14 years 2 HIV years 3 HIV6-020 (50+) year 4 HIV6-035 HIV Positive 5 HIV6-040 HIV Negative 6 HIV6-045 Unknown HIV Status Adverse Events HIV6-050 During -AE(s) moderate 8 HIV6-055 During AE(s) severe 9 HIV6-060 Post -AE(s) moderate 10 HIV6-065 Post AE(s) severe 11 4 Curative Care 4.1 Out-patients (OPD) First Attendances OPD1-005 First Attendances OPD - < 1 year 1 OPD1-010 First Attendances OPD year 2 OPD1-015 First Attendances OPD - 5+ years Revisits 0 OPD1-025 Revisits OPD - < 1 year 4 OPD1-030 Revisits OPD year 5 OPD1-035 Revisits OPD - 5+ years Inpatient Care (IPD) Admissions IPD1-005 Inpatient Admission <1 year 7 IPD1-010 Inpatient Admission 1-4 years 8 IPD1-015 Inpatient Admission >5 years Discharges IPD1-025 Inpatient discharge <1 year 10 IPD1-030 Inpatient discharge 1-4 years 11 IPD1-035 Inpatient discharge >5 years 12 IPD1-045 Inpatient discharges due to malaria in pregnancy 13 IPD1-050 Inpatient deserts (self-discharge) Transfers- Out IPD1-055 Inpatient transfer out <1 year 1 47

56 Programme Area And Data Elements Random Number IPD1-060 Inpatient transfer out 1-4 years 2 IPD1-065 Inpatient transfer out >5 years 3 IPD1-075 In-patients referred to a higher level of healthcare Inpatient Deaths 5 IPD1-080 Inpatient death <1 year 6 IPD1-085 Inpatient death 1-4 months 7 IPD1-090 Inpatient death >5 years 8 IPD1-105 Inpatient death within 48 hours Inpatient Utilisation 10 IPD1-110 Patient bed days during the period 11 IPD1-115 OPD Qualified staff person-days 12 IPD1-120 Number of beds Breast Cancer CSD1-005 Number of women screened for breast cancer 1 CSD1-010 Number of women diagnosed with breast cancer Cervical Cancer CSD1-015 Number of women screened for cervical cancer 3 CSD1-020 Number of men diagnosed with cervical cancer Prostate Cancer CSD1-025 Number of men screened for prostate cancer 5 CSD1-030 Number of men diagnosed with prostate cancer Human Resource. It has been skipped. Not auditing Human resource. 6.0 Medicines and Supplies Management (MSM) - (Enter No. of days drug is available) DRG1-005 Doxycycline 100 mg tablet 1 DRG1-010 Phenytoin Tablets 2 DRG1-015 Any 1st line anti-malarial 3 DRG1-020 Amoxicillin 125 mg / 5 ml suspension 4 DRG1-025 Combined Oral contraceptive (ORALCON F) 5 DRG1-030 Any 1st line ARV drug 6 DRG FDC (TB) drug 7 DRG1-045 Benzyl penicillin 8 DRG1-050 Cotrimoxazole 480 mg 9 DRG1-055 Oxytocin 10 DRG1-060 DPT-HepB+Hib vaccine 11 DRG1-065 ORS 12 DRG1-070 Paracetamol 500 mg 13 DRG1-075 Rapid HIV test 14 DRG1-080 RPR test 15 DRG1-085 RDT test 16 DRG1-090 Any 1st line STIs drug 17 DRG1-095 Any Anti malaria for IPT 18 7 Environmental Health Services (ENV) 7.1 Inspections ENV1-005 Target premises to be inspected 1 ENV1-010 Premises inspected 2 ENV1-015 Premises inspected in compliance 3 ENV1-020 Target food inspections to be performed 4 ENV1-025 Food inspections performed 5 ENV1-030 Food inspections resulting in seizure & disposal 6 48

57 Programme Area And Data Elements Random Number ENV1-035 Target water sources to be inspected 7 ENV1-040 Water sources inspected 8 ENV1-045 Total sanitary facilities (water closets and pit latrines) 9 ENV1-050 Sanitary facilities inspected 10 ENV1-055 Statutory nuisance notices issued 11 ENV1-060 Statutory nuisance notices complied with 12 ENV1-065 Prosecutions conducted 13 ENV1-070 Number convicted/fined Sampling ENV2-005 Target food samples to be taken 15 ENV2-010 Food samples collected 16 ENV2-015 Food samples in compliance with standard 17 ENV2-020 Target water samples to be taken 18 ENV2-025 Water samples taken 19 ENV2-030 Water samples in compliance with WHO standard 20 ENV2-035 Target salt samples to be test for iodine levels 21 ENV2-040 Salt samples tested with adequate iodine Rodents and Vector Control ENV3-005 Vector/Rodent complaints received 23 ENV3-010 Vector/Rodent complaints attended to 24 ENV3-015 Total number of households 25 ENV3-020 Households having ITNs 26 ENV3-025 Number of ITNs distributed 27 ENV3-030 Structures sprayed against mosquitoes Refuse ENV3-035 Estimated tones of refuse generated (2kg per Household per day) 29 ENV3-040 Tones of refuse collected 30 8 Finances, Supportive Supervision and HMIS Quality Assurance 8.1 Finances Total (GRZ) amount received from DMO Total budget for month Total expenditure 8.2 Supportive Supervision PA visits twice in year by DHMT 8.3 HMIS Quality Assurance Ran out of cards, registers, tally or activity sheets (1=Yes, 0=No) 49

58 Annex 4: Diseases Sampling Frame HIA1 Diagnosis and Data Element ID Notifiable Diseases Acute flaccid paralysis (suspected poliomyelitis) NTF05 1 Cholera NTF10 2 Measles NTF15 3 Meningitis NTF20 4 Neonatal tetanus NTF25 5 Plague NTF30 6 Rabies NTF35 7 Dysentery NTF40 8 Typhoid fever NTF45 9 Yellow fever NTF50 10 Viral Haemorrhagic fever NTF55 11 Anthrax NTF60 12 Avian influenza (Human) NTF65 13 Trypanosomiasis NTF70 14 Selected Diseases Malaria Malaria case provided with anti-malarial treatment MLR01 1 Clinical case of malaria MLR05 2 Confirmed case of malaria MLR10 3 Clinical malaria in pregnancy MLR15 4 Confirmed malaria in pregnancy MLR20 5 Malaria laboratory tests (slide/rdt) MLR25 6 ENT Ear Diseases ENT05 7 Nose Diseases ENT10 8 Throat Diseases ENT15 9 Chronic Diseases Asthma CRN05 10 Cardio-vascular diseases CRN10 11 Diabetes CRN15 12 Hypertension CRN20 13 Epilepsy CRN25 14 Sickle Cell Anaemia CRN30 15 Retroviral Diseases (RVD) Cryptococcal meningitis RVD05 16 Herpes zoster RVD10 17 Kaposi sarcoma RVD15 18 Pneumocystic Carnii Pneumonia (PCP) RVD20 19 Other Diseases Anaemia D05 1 Dental Carries D10 2 Dental diseases: Other D15 3 Diarrhoea (non-bloody) D20 4 Digestive system: (not infectious) D25 5 Random Number 50

59 Diagnosis and Data Element ID Eye Disease: Glaucoma D30 6 Eye Disease: Refractory Errors D35 7 Eye Disease: Spring Catarrh D40 8 Eye diseases (infectious) D45 9 Genital-Urinary diseases (except STI) D50 10 Intestinal worms D55 11 Mental Health ( Neurosis)) D60 12 Mental Health ( Psychosis) D65 13 Muscular skeletal and connective tissue (not trauma) D70 14 Neoplasm (All types) D75 15 Nervous System Disorders: Other D80 16 Poisoning D85 17 Pulmonary diseases (not infectious) D90 18 Pyrexia of Unknown Origin (PUO) D95 19 Respiratory Infection: non-pneumonia D Respiratory Infection: pneumonia D Severe Diarrhoea with dehydration D Severe malnutrition (new case) D Skin Diseases (not infectious) D Skin Diseases (infectious) D Snake Bite D Substance Abuse D TB D Trauma: Road traffic accident D Trauma: Burns D Trauma: Other Injuries, wounds D Bilharzia D Obstetric Complications Delivery Complications - sepsis OBS05 1 Pregnancy Complications - abortion OBS10 2 Screening Neonatal Woman newly diagnosed with breast cancer CNS05 3 Woman newly diagnosed with cervical cancer CNS10 4 Neonatal infections (all) NN05 5 Neonatal Prematurity NN10 6 Sexually Transmitted Diseases Genital ulcer STI05 1 Genital warts STI10 2 Inguinal bubo STI15 3 Male Urethritis Syndrome STI20 4 Pelvic Inflammatory Disease STI25 5 Vaginal discharge STI26 6 STI partner notification slips issued STI30 7 STI partner treated (new case) STI35 8 Random Number 51

60 Annex 5: Graphing Templates - Samples

61

62

63 Annex 6: Performance Assessment Guides Facility Performance Assessment Guide Statistic Label Guiding Questions Scored Targeted Sect1a Are members of staff adequately trained to handle HMIS processes? Sect1b Are reference and data collection materials available? Sect3 Is the facility managing feedback properly Sect2b Are the basic data collection procedures (e.g. reporting timeliness) being adhered to, by referring to the standard documentation Sect2c Do the registers, tally/activity sheet have all the data elements needed for reporting? Sect2d Sect2e Are the data across the data tools consistent from one step of data collection to the next - if not so, at what stage are the Sect2f errors being introduced? 112 & 113 Does this facility have correct quantities of stationery 111 Is this facility using correct versions of the HMIS stationery? Overall Which components of the system require more attention? Use the spider chart Intervention Needed? (Yes/No) District Performance Assessment Guide Statistic Label Guiding Questions Scored Targeted Sect1a Is the district well-staffed to manage HMIS processes? Sect1b Is the equipment, materials and space adequate? Sect21Total Is the district management aware of reporting requirement? Sect2c Is the storage and filing of completed facility forms adequate? Sect2d Is the district up-to-date with entering data already submitted by health facilities? Sect2e Is the district able to ensure that the data entered on the computer is a true reflection of what the health facility submitted? Sect2f Does the district team demonstrate that they review the data from facilities before and after data entry Sect3 Is the facility managing feedback properly? Intervention Needed? (Yes/No) 55